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2401.09350 | 417 | Substituting t into Equation (C.1) gives the desired result.
# C.4 Hoeffdingâs Inequality
We need the following result, known as Hoeffdingâs Lemma, to present Ho- effdingâs inequality.
Lemma C.5 Let X be a zero-mean random variable that takes values in [a, b]. For any t > 0:
E [el*] < exp (eo).
Proof. By convexity of etx and given x â [a, b] we have that:
179
ââ
180
C Concentration of Measure
etx ⤠b â x b â a eta + x â a b â a etb.
Taking the expectation of both sides, we arrive at:
b a E [e'*| < ta tb. e} ba. baâ
# E
To conclude the proof, we first write the right-hand-side as exp(h(t(b â a))) where:
a / b a. h(x) boat t 8 (53 - pat ).
By expanding h(x) using Taylorâs theorem, it can be shown that h(x) ⤠x2/8. ââ That completes the proof.
We are ready to present Hoeffdingâs inequality. | 2401.09350#417 | Foundations of Vector Retrieval | Vectors are universal mathematical objects that can represent text, images,
speech, or a mix of these data modalities. That happens regardless of whether
data is represented by hand-crafted features or learnt embeddings. Collect a
large enough quantity of such vectors and the question of retrieval becomes
urgently relevant: Finding vectors that are more similar to a query vector.
This monograph is concerned with the question above and covers fundamental
concepts along with advanced data structures and algorithms for vector
retrieval. In doing so, it recaps this fascinating topic and lowers barriers of
entry into this rich area of research. | http://arxiv.org/pdf/2401.09350 | Sebastian Bruch | cs.DS, cs.IR | null | null | cs.DS | 20240117 | 20240117 | [] | [
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] | {
"authors": "Sebastian Bruch",
"chunk_id": 417,
"doc_id": "2401.09350",
"primary_category": "cs.DS",
"published": 20240117,
"source": "http://arxiv.org/pdf/2401.09350",
"summary": "Vectors are universal mathematical objects that can represent text, images,\nspeech, or a mix of these data modalities. That happens regardless of whether\ndata is represented by hand-crafted features or learnt embeddings. Collect a\nlarge enough quantity of such vectors and the question of retrieval becomes\nurgently relevant: Finding vectors that are more similar to a query vector.\nThis monograph is concerned with the question above and covers fundamental\nconcepts along with advanced data structures and algorithms for vector\nretrieval. In doing so, it recaps this fascinating topic and lowers barriers of\nentry into this rich area of research.",
"text": "Substituting t into Equation (C.1) gives the desired result.\n# C.4 Hoeffdingâs Inequality\nWe need the following result, known as Hoeffdingâs Lemma, to present Ho- effdingâs inequality.\nLemma C.5 Let X be a zero-mean random variable that takes values in [a, b]. For any t > 0:\nE [el*] < exp (eo).\nProof. By convexity of etx and given x â [a, b] we have that:\n179\nââ\n180\nC Concentration of Measure\netx ⤠b â x b â a eta + x â a b â a etb.\nTaking the expectation of both sides, we arrive at:\nb a E [e'*| < ta tb. e} ba. baâ\n# E\nTo conclude the proof, we first write the right-hand-side as exp(h(t(b â a))) where:\na / b a. h(x) boat t 8 (53 - pat ).\nBy expanding h(x) using Taylorâs theorem, it can be shown that h(x) ⤠x2/8. ââ That completes the proof.\nWe are ready to present Hoeffdingâs inequality.",
"title": "Foundations of Vector Retrieval",
"year": 2024
} |
2401.09350 | 418 | We are ready to present Hoeffdingâs inequality.
Lemma C.6 Let {Xi}n mean µ and suppose Xi â [a, b] almost surely. For all ϵ > 0: i=1 be a sequence of iid random variables with finite
2 _â 2ne P [pe â | > | < 2exp (a ap):
Proof. Let X = 1/n >,
i Xi â µ. Observe by Markovâs inequality that:
P[X >¢/=P [e'* > e] <e~ Efeâ).
By independence of Xiâs and the application of Lemma C.5:
t(Xi-4) Te a Te [-*) Too ("5") 2(h â a)2 â *), E{e'*] =E = exp (
We have shown that:
2(bâ a)? P[LX > < exp (-te+ ren). 8n
That statement holds for all values of t and in particular one that minimizes the right-hand-side. Solving for that value of t gives us t = 4nϵ/(b â a2), which implies:
C.5 Bennetâs Inequality | 2401.09350#418 | Foundations of Vector Retrieval | Vectors are universal mathematical objects that can represent text, images,
speech, or a mix of these data modalities. That happens regardless of whether
data is represented by hand-crafted features or learnt embeddings. Collect a
large enough quantity of such vectors and the question of retrieval becomes
urgently relevant: Finding vectors that are more similar to a query vector.
This monograph is concerned with the question above and covers fundamental
concepts along with advanced data structures and algorithms for vector
retrieval. In doing so, it recaps this fascinating topic and lowers barriers of
entry into this rich area of research. | http://arxiv.org/pdf/2401.09350 | Sebastian Bruch | cs.DS, cs.IR | null | null | cs.DS | 20240117 | 20240117 | [] | [
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] | {
"authors": "Sebastian Bruch",
"chunk_id": 418,
"doc_id": "2401.09350",
"primary_category": "cs.DS",
"published": 20240117,
"source": "http://arxiv.org/pdf/2401.09350",
"summary": "Vectors are universal mathematical objects that can represent text, images,\nspeech, or a mix of these data modalities. That happens regardless of whether\ndata is represented by hand-crafted features or learnt embeddings. Collect a\nlarge enough quantity of such vectors and the question of retrieval becomes\nurgently relevant: Finding vectors that are more similar to a query vector.\nThis monograph is concerned with the question above and covers fundamental\nconcepts along with advanced data structures and algorithms for vector\nretrieval. In doing so, it recaps this fascinating topic and lowers barriers of\nentry into this rich area of research.",
"text": "We are ready to present Hoeffdingâs inequality.\nLemma C.6 Let {Xi}n mean µ and suppose Xi â [a, b] almost surely. For all ϵ > 0: i=1 be a sequence of iid random variables with finite\n2 _â 2ne P [pe â | > | < 2exp (a ap):\nProof. Let X = 1/n >,\ni Xi â µ. Observe by Markovâs inequality that:\nP[X >¢/=P [e'* > e] <e~ Efeâ).\nBy independence of Xiâs and the application of Lemma C.5:\nt(Xi-4) Te a Te [-*) Too (\"5\") 2(h â a)2 â *), E{e'*] =E = exp (\nWe have shown that:\n2(bâ a)? P[LX > < exp (-te+ ren). 8n\nThat statement holds for all values of t and in particular one that minimizes the right-hand-side. Solving for that value of t gives us t = 4nϵ/(b â a2), which implies:\nC.5 Bennetâs Inequality",
"title": "Foundations of Vector Retrieval",
"year": 2024
} |
2401.09350 | 419 | C.5 Bennetâs Inequality
P[X ⥠ϵ] ⤠eâ 2nϵ2 By a symmetric argument we can bound P[X ⤠âϵ]. The claim follows by ââ the union bound over the two cases.
# C.5 Bennetâs Inequality
Lemma C.7 Let {Xi}n with zero mean and finite variance Ï2 for all i. Then: i=1 be a sequence of independent random variables i . Assume that |Xi| ⤠a almost surely
2 [Sx zd] <oo(-Sa(S)).
where h(x) = (1+ 2x) log(1 +2) â2 and o? =),
i Ï2 i .
Proof. As usual, we take advantage of Markovâs inequality to write:
P [Lx > â| <eME [a=] =eE Ie] =e Ifs [e*']
Using the Taylor expansion of ex, we obtain:
ple") =8[5 â8 OO \koryayk~3 AY E[X? Xk?) =1+ Y âr 2 a? BS Mak =l+ a k=2 2 o? = 1+ 5(e â1âAa) 2 < exp (25 (e â 1 Aa))
181
182
C Concentration of Measure | 2401.09350#419 | Foundations of Vector Retrieval | Vectors are universal mathematical objects that can represent text, images,
speech, or a mix of these data modalities. That happens regardless of whether
data is represented by hand-crafted features or learnt embeddings. Collect a
large enough quantity of such vectors and the question of retrieval becomes
urgently relevant: Finding vectors that are more similar to a query vector.
This monograph is concerned with the question above and covers fundamental
concepts along with advanced data structures and algorithms for vector
retrieval. In doing so, it recaps this fascinating topic and lowers barriers of
entry into this rich area of research. | http://arxiv.org/pdf/2401.09350 | Sebastian Bruch | cs.DS, cs.IR | null | null | cs.DS | 20240117 | 20240117 | [] | [
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] | {
"authors": "Sebastian Bruch",
"chunk_id": 419,
"doc_id": "2401.09350",
"primary_category": "cs.DS",
"published": 20240117,
"source": "http://arxiv.org/pdf/2401.09350",
"summary": "Vectors are universal mathematical objects that can represent text, images,\nspeech, or a mix of these data modalities. That happens regardless of whether\ndata is represented by hand-crafted features or learnt embeddings. Collect a\nlarge enough quantity of such vectors and the question of retrieval becomes\nurgently relevant: Finding vectors that are more similar to a query vector.\nThis monograph is concerned with the question above and covers fundamental\nconcepts along with advanced data structures and algorithms for vector\nretrieval. In doing so, it recaps this fascinating topic and lowers barriers of\nentry into this rich area of research.",
"text": "C.5 Bennetâs Inequality\nP[X ⥠ϵ] ⤠eâ 2nϵ2 By a symmetric argument we can bound P[X ⤠âϵ]. The claim follows by ââ the union bound over the two cases.\n# C.5 Bennetâs Inequality\nLemma C.7 Let {Xi}n with zero mean and finite variance Ï2 for all i. Then: i=1 be a sequence of independent random variables i . Assume that |Xi| ⤠a almost surely\n2 [Sx zd] <oo(-Sa(S)).\nwhere h(x) = (1+ 2x) log(1 +2) â2 and o? =),\ni Ï2 i .\nProof. As usual, we take advantage of Markovâs inequality to write:\nP [Lx > â| <eME [a=] =eE Ie] =e Ifs [e*']\nUsing the Taylor expansion of ex, we obtain:\nple\") =8[5 â8 OO \\koryayk~3 AY E[X? Xk?) =1+ Y âr 2 a? BS Mak =l+ a k=2 2 o? = 1+ 5(e â1âAa) 2 < exp (25 (e â 1 Aa))\n181\n182\nC Concentration of Measure",
"title": "Foundations of Vector Retrieval",
"year": 2024
} |
2401.09350 | 420 | 181
182
C Concentration of Measure
Putting it all together:
2 âX Od P[> Xi2t|<e âTex (3 (e-1-da)) i i âAt ova =e exp (Sle -â1 da)).
# P
This inequality holds for all values of λ, and in particular one that minimizes the right-hand-side. Setting the derivative of the right-hand-side to 0 and ââ solving for λ leads to the desired result.
Appendix D Linear Algebra Review
Abstract This appendix reviews basic concepts from Linear Algebra that are useful in digesting the material in this monograph.
# D.1 Inner Product
Denote by H a vector space. An inner product â¨Â·, ·⩠: H à H â R is a function with the following properties: | 2401.09350#420 | Foundations of Vector Retrieval | Vectors are universal mathematical objects that can represent text, images,
speech, or a mix of these data modalities. That happens regardless of whether
data is represented by hand-crafted features or learnt embeddings. Collect a
large enough quantity of such vectors and the question of retrieval becomes
urgently relevant: Finding vectors that are more similar to a query vector.
This monograph is concerned with the question above and covers fundamental
concepts along with advanced data structures and algorithms for vector
retrieval. In doing so, it recaps this fascinating topic and lowers barriers of
entry into this rich area of research. | http://arxiv.org/pdf/2401.09350 | Sebastian Bruch | cs.DS, cs.IR | null | null | cs.DS | 20240117 | 20240117 | [] | [
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] | {
"authors": "Sebastian Bruch",
"chunk_id": 420,
"doc_id": "2401.09350",
"primary_category": "cs.DS",
"published": 20240117,
"source": "http://arxiv.org/pdf/2401.09350",
"summary": "Vectors are universal mathematical objects that can represent text, images,\nspeech, or a mix of these data modalities. That happens regardless of whether\ndata is represented by hand-crafted features or learnt embeddings. Collect a\nlarge enough quantity of such vectors and the question of retrieval becomes\nurgently relevant: Finding vectors that are more similar to a query vector.\nThis monograph is concerned with the question above and covers fundamental\nconcepts along with advanced data structures and algorithms for vector\nretrieval. In doing so, it recaps this fascinating topic and lowers barriers of\nentry into this rich area of research.",
"text": "181\n182\nC Concentration of Measure\nPutting it all together:\n2 âX Od P[> Xi2t|<e âTex (3 (e-1-da)) i i âAt ova =e exp (Sle -â1 da)).\n# P\nThis inequality holds for all values of λ, and in particular one that minimizes the right-hand-side. Setting the derivative of the right-hand-side to 0 and ââ solving for λ leads to the desired result.\nAppendix D Linear Algebra Review\nAbstract This appendix reviews basic concepts from Linear Algebra that are useful in digesting the material in this monograph.\n# D.1 Inner Product\nDenote by H a vector space. An inner product â¨Â·, ·⩠: H à H â R is a function with the following properties:",
"title": "Foundations of Vector Retrieval",
"year": 2024
} |
2401.09350 | 421 | # D.1 Inner Product
Denote by H a vector space. An inner product â¨Â·, ·⩠: H à H â R is a function with the following properties:
â u â H, â¨u, u⩠⥠0; ⢠â u â H, â¨u, uâ© = 0 â u = 0; ⢠â u, v â H, â¨u, vâ© = â¨v, uâ©; and, ⢠â u, v, w â H, and α, β â R, â¨Î±u + βv, wâ© = αâ¨u, wâ© + βâ¨v, wâ©.
We call H together with the inner product (-,-) an inner product space. As an example, when H = R%, given two vectors u = al ue; and v = d > : ; a . Yi-1 vie, where e;âs are the standard basis vectors, the following is an inner product:
(u,v) = Ss UiV;- i=1
We say two vectors u and v in an inner product space are orthogonal if their inner product is 0: â¨u, vâ© = 0.
# D.2 Norms | 2401.09350#421 | Foundations of Vector Retrieval | Vectors are universal mathematical objects that can represent text, images,
speech, or a mix of these data modalities. That happens regardless of whether
data is represented by hand-crafted features or learnt embeddings. Collect a
large enough quantity of such vectors and the question of retrieval becomes
urgently relevant: Finding vectors that are more similar to a query vector.
This monograph is concerned with the question above and covers fundamental
concepts along with advanced data structures and algorithms for vector
retrieval. In doing so, it recaps this fascinating topic and lowers barriers of
entry into this rich area of research. | http://arxiv.org/pdf/2401.09350 | Sebastian Bruch | cs.DS, cs.IR | null | null | cs.DS | 20240117 | 20240117 | [] | [
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] | {
"authors": "Sebastian Bruch",
"chunk_id": 421,
"doc_id": "2401.09350",
"primary_category": "cs.DS",
"published": 20240117,
"source": "http://arxiv.org/pdf/2401.09350",
"summary": "Vectors are universal mathematical objects that can represent text, images,\nspeech, or a mix of these data modalities. That happens regardless of whether\ndata is represented by hand-crafted features or learnt embeddings. Collect a\nlarge enough quantity of such vectors and the question of retrieval becomes\nurgently relevant: Finding vectors that are more similar to a query vector.\nThis monograph is concerned with the question above and covers fundamental\nconcepts along with advanced data structures and algorithms for vector\nretrieval. In doing so, it recaps this fascinating topic and lowers barriers of\nentry into this rich area of research.",
"text": "# D.1 Inner Product\nDenote by H a vector space. An inner product â¨Â·, ·⩠: H à H â R is a function with the following properties:\nâ u â H, â¨u, u⩠⥠0; ⢠â u â H, â¨u, uâ© = 0 â u = 0; ⢠â u, v â H, â¨u, vâ© = â¨v, uâ©; and, ⢠â u, v, w â H, and α, β â R, â¨Î±u + βv, wâ© = αâ¨u, wâ© + βâ¨v, wâ©.\nWe call H together with the inner product (-,-) an inner product space. As an example, when H = R%, given two vectors u = al ue; and v = d > : ; a . Yi-1 vie, where e;âs are the standard basis vectors, the following is an inner product:\n(u,v) = Ss UiV;- i=1\nWe say two vectors u and v in an inner product space are orthogonal if their inner product is 0: â¨u, vâ© = 0.\n# D.2 Norms",
"title": "Foundations of Vector Retrieval",
"year": 2024
} |
2401.09350 | 422 | # D.2 Norms
A function Φ : H â R+ is a norm on H if it has the following properties: ⢠Definiteness: For all u â H, Φ(u) = 0 â u = 0;
183
184
D Linear Algebra Review
Homogeneity: For all u â H and α â R, Φ(αu) = |α|Φ(u); and, ⢠Triangle inequality: â u, v â H, Φ(u + v) ⤠Φ(u) + Φ(v).
Examples include the absolute value on R, and the Lp norm (for p ⥠1)
on Rd denoted by â¥Â·â¥p and defined as:
d a lullp = (Solusl) *- i=l
Instances of Lp include the commonly used L1, L2 (Euclidean), and Lâ norms, where â¥uâ¥â = maxi|ui|.
Note that, when H is an inner product space, then the function â¥u⥠=
Note that, when H is an inner product space, then the function ||u|| = /(u,u) is a norm.
# D.3 Distance | 2401.09350#422 | Foundations of Vector Retrieval | Vectors are universal mathematical objects that can represent text, images,
speech, or a mix of these data modalities. That happens regardless of whether
data is represented by hand-crafted features or learnt embeddings. Collect a
large enough quantity of such vectors and the question of retrieval becomes
urgently relevant: Finding vectors that are more similar to a query vector.
This monograph is concerned with the question above and covers fundamental
concepts along with advanced data structures and algorithms for vector
retrieval. In doing so, it recaps this fascinating topic and lowers barriers of
entry into this rich area of research. | http://arxiv.org/pdf/2401.09350 | Sebastian Bruch | cs.DS, cs.IR | null | null | cs.DS | 20240117 | 20240117 | [] | [
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] | {
"authors": "Sebastian Bruch",
"chunk_id": 422,
"doc_id": "2401.09350",
"primary_category": "cs.DS",
"published": 20240117,
"source": "http://arxiv.org/pdf/2401.09350",
"summary": "Vectors are universal mathematical objects that can represent text, images,\nspeech, or a mix of these data modalities. That happens regardless of whether\ndata is represented by hand-crafted features or learnt embeddings. Collect a\nlarge enough quantity of such vectors and the question of retrieval becomes\nurgently relevant: Finding vectors that are more similar to a query vector.\nThis monograph is concerned with the question above and covers fundamental\nconcepts along with advanced data structures and algorithms for vector\nretrieval. In doing so, it recaps this fascinating topic and lowers barriers of\nentry into this rich area of research.",
"text": "# D.2 Norms\nA function Φ : H â R+ is a norm on H if it has the following properties: ⢠Definiteness: For all u â H, Φ(u) = 0 â u = 0;\n183\n184\nD Linear Algebra Review\nHomogeneity: For all u â H and α â R, Φ(αu) = |α|Φ(u); and, ⢠Triangle inequality: â u, v â H, Φ(u + v) ⤠Φ(u) + Φ(v).\nExamples include the absolute value on R, and the Lp norm (for p ⥠1)\non Rd denoted by â¥Â·â¥p and defined as:\nd a lullp = (Solusl) *- i=l\nInstances of Lp include the commonly used L1, L2 (Euclidean), and Lâ norms, where â¥uâ¥â = maxi|ui|.\nNote that, when H is an inner product space, then the function â¥u⥠=\nNote that, when H is an inner product space, then the function ||u|| = /(u,u) is a norm.\n# D.3 Distance",
"title": "Foundations of Vector Retrieval",
"year": 2024
} |
2401.09350 | 423 | Note that, when H is an inner product space, then the function ||u|| = /(u,u) is a norm.
# D.3 Distance
A norm on a vector space induces a notion of distance between two vectors. Concretely, if H is a normed space equipped with â¥Â·â¥, then we define the distance between two vectors u, v â H as follows:
δ(u, v) = â¥u â vâ¥.
# D.4 Orthogonal Projection
Lemma D.1 Let H be an inner product space and suppose u â H and u ̸= 0. Any vector v â H can be uniquely decomposed along u as:
v = v⥠+ vâ¥,
such that â¨vâ¥, vâ¥â© = 0. Additionally:
v⥠= â¨u, vâ© â¨u, uâ© u,
and v⥠= v â vâ¥.
Proof. Let v⥠= αu and v⥠= v â vâ¥. Because v⥠and v⥠are orthogonal, we deduce that: | 2401.09350#423 | Foundations of Vector Retrieval | Vectors are universal mathematical objects that can represent text, images,
speech, or a mix of these data modalities. That happens regardless of whether
data is represented by hand-crafted features or learnt embeddings. Collect a
large enough quantity of such vectors and the question of retrieval becomes
urgently relevant: Finding vectors that are more similar to a query vector.
This monograph is concerned with the question above and covers fundamental
concepts along with advanced data structures and algorithms for vector
retrieval. In doing so, it recaps this fascinating topic and lowers barriers of
entry into this rich area of research. | http://arxiv.org/pdf/2401.09350 | Sebastian Bruch | cs.DS, cs.IR | null | null | cs.DS | 20240117 | 20240117 | [] | [
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] | {
"authors": "Sebastian Bruch",
"chunk_id": 423,
"doc_id": "2401.09350",
"primary_category": "cs.DS",
"published": 20240117,
"source": "http://arxiv.org/pdf/2401.09350",
"summary": "Vectors are universal mathematical objects that can represent text, images,\nspeech, or a mix of these data modalities. That happens regardless of whether\ndata is represented by hand-crafted features or learnt embeddings. Collect a\nlarge enough quantity of such vectors and the question of retrieval becomes\nurgently relevant: Finding vectors that are more similar to a query vector.\nThis monograph is concerned with the question above and covers fundamental\nconcepts along with advanced data structures and algorithms for vector\nretrieval. In doing so, it recaps this fascinating topic and lowers barriers of\nentry into this rich area of research.",
"text": "Note that, when H is an inner product space, then the function ||u|| = /(u,u) is a norm.\n# D.3 Distance\nA norm on a vector space induces a notion of distance between two vectors. Concretely, if H is a normed space equipped with â¥Â·â¥, then we define the distance between two vectors u, v â H as follows:\nδ(u, v) = â¥u â vâ¥.\n# D.4 Orthogonal Projection\nLemma D.1 Let H be an inner product space and suppose u â H and u ̸= 0. Any vector v â H can be uniquely decomposed along u as:\nv = v⥠+ vâ¥,\nsuch that â¨vâ¥, vâ¥â© = 0. Additionally:\nv⥠= â¨u, vâ© â¨u, uâ© u,\nand v⥠= v â vâ¥.\nProof. Let v⥠= αu and v⥠= v â vâ¥. Because v⥠and v⥠are orthogonal, we deduce that:",
"title": "Foundations of Vector Retrieval",
"year": 2024
} |
2401.09350 | 424 | Proof. Let v⥠= αu and v⥠= v â vâ¥. Because v⥠and v⥠are orthogonal, we deduce that:
â¨vâ¥, vâ¥â© = 0 =â â¨Î±u, vâ¥â© = 0 =â â¨u, vâ¥â© = 0.
That implies:
D.4 Orthogonal Projection
â¨v, uâ© = αâ¨u, uâ© =â α = â¨u, vâ© â¨u, uâ© ,
so that:
v⥠= â¨u, vâ© â¨u, uâ© u.
We prove the uniqueness of the decomposition by contradiction. Suppose there exists another decomposition of v to vⲠ⥠+ vâ² â¥. Then: | 2401.09350#424 | Foundations of Vector Retrieval | Vectors are universal mathematical objects that can represent text, images,
speech, or a mix of these data modalities. That happens regardless of whether
data is represented by hand-crafted features or learnt embeddings. Collect a
large enough quantity of such vectors and the question of retrieval becomes
urgently relevant: Finding vectors that are more similar to a query vector.
This monograph is concerned with the question above and covers fundamental
concepts along with advanced data structures and algorithms for vector
retrieval. In doing so, it recaps this fascinating topic and lowers barriers of
entry into this rich area of research. | http://arxiv.org/pdf/2401.09350 | Sebastian Bruch | cs.DS, cs.IR | null | null | cs.DS | 20240117 | 20240117 | [] | [
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] | {
"authors": "Sebastian Bruch",
"chunk_id": 424,
"doc_id": "2401.09350",
"primary_category": "cs.DS",
"published": 20240117,
"source": "http://arxiv.org/pdf/2401.09350",
"summary": "Vectors are universal mathematical objects that can represent text, images,\nspeech, or a mix of these data modalities. That happens regardless of whether\ndata is represented by hand-crafted features or learnt embeddings. Collect a\nlarge enough quantity of such vectors and the question of retrieval becomes\nurgently relevant: Finding vectors that are more similar to a query vector.\nThis monograph is concerned with the question above and covers fundamental\nconcepts along with advanced data structures and algorithms for vector\nretrieval. In doing so, it recaps this fascinating topic and lowers barriers of\nentry into this rich area of research.",
"text": "Proof. Let v⥠= αu and v⥠= v â vâ¥. Because v⥠and v⥠are orthogonal, we deduce that:\nâ¨vâ¥, vâ¥â© = 0 =â â¨Î±u, vâ¥â© = 0 =â â¨u, vâ¥â© = 0.\nThat implies:\nD.4 Orthogonal Projection\nâ¨v, uâ© = αâ¨u, uâ© =â α = â¨u, vâ© â¨u, uâ© ,\nso that:\nv⥠= â¨u, vâ© â¨u, uâ© u.\nWe prove the uniqueness of the decomposition by contradiction. Suppose there exists another decomposition of v to vⲠ⥠+ vâ² â¥. Then:",
"title": "Foundations of Vector Retrieval",
"year": 2024
} |
2311.04254 | 2 | # ABSTRACT
Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable lan- guage sequences referred to as âthoughtsâ. An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. How- ever, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called âEverything â of existing thought of Thoughtsâ (XOT) to defy the law of âPenrose triangle paradigms. XOT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMsâ capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XOT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluate XOT on several challenging problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XOT significantly outperforms existing approaches in various dimensions, showcasing its remark- able proficiency in addressing complex problems across diverse domains. | 2311.04254#2 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 2,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "# ABSTRACT\nRecent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable lan- guage sequences referred to as âthoughtsâ. An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. How- ever, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called âEverything â of existing thought of Thoughtsâ (XOT) to defy the law of âPenrose triangle paradigms. XOT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMsâ capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XOT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluate XOT on several challenging problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XOT significantly outperforms existing approaches in various dimensions, showcasing its remark- able proficiency in addressing complex problems across diverse domains.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 3 | 1
# INTRODUCTION
Recent advancements in Large Lan- guage Models (LLMs) have greatly ad- vanced problem solving in diverse do- mains such as mathematical reasoning Frieder et al. (2023), knowledge rea- soning Omar et al. (2023), root cause analysis Chen et al. (2023) and causal inference Kıcıman et al. (2023), etc.. This progress can be largely attributed to the technique of decomposing intri- cate problems into smaller language se- quences referred to as âthoughtsâ. Through a step-by-step inference process involving the use of prompts, each thought functions as an intermediate stage, contributing to the simplification of tack- ling complex problems to fulfill the problemâs ultimate objective.
Table 1: Comparisons of different prompting paradigms. Paradigm Performance Efficiency Flexibility IO CoT CoT-SC ToT GoT XOT
Effective design of thought steps toward complex problem-solving and reasoning, whether for hu- mans or LLMs, should prioritize three crucial aspects, namely:
⢠Performance. Performance is the accuracy of the solution to a problem, including the precision of each thought at intermediate stages. This metric holds paramount importance for problem-solving.
1 | 2311.04254#3 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 3,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "1\n# INTRODUCTION\nRecent advancements in Large Lan- guage Models (LLMs) have greatly ad- vanced problem solving in diverse do- mains such as mathematical reasoning Frieder et al. (2023), knowledge rea- soning Omar et al. (2023), root cause analysis Chen et al. (2023) and causal inference Kıcıman et al. (2023), etc.. This progress can be largely attributed to the technique of decomposing intri- cate problems into smaller language se- quences referred to as âthoughtsâ. Through a step-by-step inference process involving the use of prompts, each thought functions as an intermediate stage, contributing to the simplification of tack- ling complex problems to fulfill the problemâs ultimate objective.\nTable 1: Comparisons of different prompting paradigms. Paradigm Performance Efficiency Flexibility IO CoT CoT-SC ToT GoT XOT\nEffective design of thought steps toward complex problem-solving and reasoning, whether for hu- mans or LLMs, should prioritize three crucial aspects, namely:\n⢠Performance. Performance is the accuracy of the solution to a problem, including the precision of each thought at intermediate stages. This metric holds paramount importance for problem-solving.\n1",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 4 | ⢠Performance. Performance is the accuracy of the solution to a problem, including the precision of each thought at intermediate stages. This metric holds paramount importance for problem-solving.
1
⢠Efficiency. Efficiency relates to the number of LLM inference calls required to solve a single problem. Minimizing this aspect is crucial due to the high computational cost associated with LLM inference, thereby reducing the overall number of cost.
⢠Flexibility. Flexibility in thought topology refers to the diverse structures that can be employed by LLMs when organizing thoughts for problem-solving. These structures may include chains, trees, or even graphs, mirroring human thought processes. Enabling more flexible thought struc- tures enhances the capacity of LLMs for divergent and creative thinking, which is particularly advantageous in addressing complex problems, especially those with multiple potential solutions. | 2311.04254#4 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 4,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "⢠Performance. Performance is the accuracy of the solution to a problem, including the precision of each thought at intermediate stages. This metric holds paramount importance for problem-solving.\n1\n⢠Efficiency. Efficiency relates to the number of LLM inference calls required to solve a single problem. Minimizing this aspect is crucial due to the high computational cost associated with LLM inference, thereby reducing the overall number of cost.\n⢠Flexibility. Flexibility in thought topology refers to the diverse structures that can be employed by LLMs when organizing thoughts for problem-solving. These structures may include chains, trees, or even graphs, mirroring human thought processes. Enabling more flexible thought struc- tures enhances the capacity of LLMs for divergent and creative thinking, which is particularly advantageous in addressing complex problems, especially those with multiple potential solutions.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 5 | There exist several thought generation paradigms, such as Chain-of-Thought (CoT) Wei et al. (2022), Tree-of-Thought (ToT) Yao et al. (2023), and Graph-of-Thought (GoT), etc.. However, these paradigms each have their limitations and cannot simultaneously achieve all the three desired attributes, as illustrated in Table 1. Specifically, direct Input-Output (IO) prompting is suitable pri- marily for simple problem-solving scenarios with single-step processes, lacking both in performance and flexibility. CoT and self-consistency CoT (CoT-SC) enable step-by-step problem solving, result- ing in modest performance improvements, but they are confined to linear thought structures, limiting their flexibility. In contrast, ToT and GoT permit more versatile thought topologies, accommodating tree-like or graph-like structures. However, these paradigms require the evaluation of intermediate thought steps through LLM itself, incurring significant computational costs and inefficiencies due to multiple LLM calls. These paradigms are constrained by a law analogous to the âPenrose triangle â, wherein they can achieve a maximum of two out of the three attributes, and none of them can | 2311.04254#5 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 5,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "There exist several thought generation paradigms, such as Chain-of-Thought (CoT) Wei et al. (2022), Tree-of-Thought (ToT) Yao et al. (2023), and Graph-of-Thought (GoT), etc.. However, these paradigms each have their limitations and cannot simultaneously achieve all the three desired attributes, as illustrated in Table 1. Specifically, direct Input-Output (IO) prompting is suitable pri- marily for simple problem-solving scenarios with single-step processes, lacking both in performance and flexibility. CoT and self-consistency CoT (CoT-SC) enable step-by-step problem solving, result- ing in modest performance improvements, but they are confined to linear thought structures, limiting their flexibility. In contrast, ToT and GoT permit more versatile thought topologies, accommodating tree-like or graph-like structures. However, these paradigms require the evaluation of intermediate thought steps through LLM itself, incurring significant computational costs and inefficiencies due to multiple LLM calls. These paradigms are constrained by a law analogous to the âPenrose triangle â, wherein they can achieve a maximum of two out of the three attributes, and none of them can",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 6 | We propose a novel solution called âEverything of Thoughtsâ (XOT) to address the limitations of conventional thought frameworks, enhancing essential attributes of thought generation, includ- ing performance, efficiency, and flexibility for LLM inference.1 XOT leverages reinforcement learning (RL) Li (2017) and Monte Carlo Tree Search (MCTS) Silver et al. (2017), in conjunc- tion with lightweight policy and value networks, to pretrain on specific tasks for thought search- ing and subsequently generalize to new problems. This pretraining effectively integrates external domain knowledge into the âthoughtsâ provided to LLMs, expanding their problem-solving capa- bilities, and thereby significantly improving Performance. Once trained, XOT efficiently performs thought searching using MCTS with cost-effective policy and value networks for exploration and au- tonomously generates complete cognitive mappings for LLMs. It then employs a MCTS-LLM col- laborative thought revision process to further improve the thought quality while minimizing LLM interactions. This eliminates the need for LLMs to explore and evaluate thoughts themselves, as required by ToT and GoT, enhancing XOTâs Efficiency. Furthermore, | 2311.04254#6 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 6,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "We propose a novel solution called âEverything of Thoughtsâ (XOT) to address the limitations of conventional thought frameworks, enhancing essential attributes of thought generation, includ- ing performance, efficiency, and flexibility for LLM inference.1 XOT leverages reinforcement learning (RL) Li (2017) and Monte Carlo Tree Search (MCTS) Silver et al. (2017), in conjunc- tion with lightweight policy and value networks, to pretrain on specific tasks for thought search- ing and subsequently generalize to new problems. This pretraining effectively integrates external domain knowledge into the âthoughtsâ provided to LLMs, expanding their problem-solving capa- bilities, and thereby significantly improving Performance. Once trained, XOT efficiently performs thought searching using MCTS with cost-effective policy and value networks for exploration and au- tonomously generates complete cognitive mappings for LLMs. It then employs a MCTS-LLM col- laborative thought revision process to further improve the thought quality while minimizing LLM interactions. This eliminates the need for LLMs to explore and evaluate thoughts themselves, as required by ToT and GoT, enhancing XOTâs Efficiency. Furthermore,",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 7 | This eliminates the need for LLMs to explore and evaluate thoughts themselves, as required by ToT and GoT, enhancing XOTâs Efficiency. Furthermore, MCTS demonstrates remark- able Flexibility as it can explore various thought topologies, including graph structures akin to those employed in human mind mapping processes Faste & Lin (2012); Jamieson (2012). This enables diverse and creative thinking for LLMs, making it particularly valuable when dealing with complex thought structures or tasks featuring multiple potential solutions. By concurrently achieving supe- rior performance, efficiency, and flexibility, XOT challenges the constraints posed by the âPenrose triangle | 2311.04254#7 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 7,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "This eliminates the need for LLMs to explore and evaluate thoughts themselves, as required by ToT and GoT, enhancing XOTâs Efficiency. Furthermore, MCTS demonstrates remark- able Flexibility as it can explore various thought topologies, including graph structures akin to those employed in human mind mapping processes Faste & Lin (2012); Jamieson (2012). This enables diverse and creative thinking for LLMs, making it particularly valuable when dealing with complex thought structures or tasks featuring multiple potential solutions. By concurrently achieving supe- rior performance, efficiency, and flexibility, XOT challenges the constraints posed by the âPenrose triangle",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 8 | We comprehensively evaluate XOT across a diverse range of challenging problem-solving tasks, namely Game of 24, 8-Puzzle, and Pocket Cube. Our experimental results consistently showcase XOTâs superior performance, and its capacity to provide multiple solutions to problems efficiently with just a few LLM calls. These findings establish XOT as an effective thought generation ap- proach, paving the way for new avenues in LLMsâ problem-solving capabilities.
# 2 BACKGROUND
Thought for LLMs. Addressing complex problems often entails breaking down the overarching ob- jective into multiple intermediary steps. The outcomes or cognitive processes associated with each step are thoughts, which can be expressed as linguistic prompt sequences for LLMs to facilitate problem-solving. Structures of these thought may take various forms, including linear chains, hier- archical trees, or interconnected graphs, depending on how the thoughts are organized to advance towards a solution.
1We named it âEverything of Thoughtsâ to signify its three comprehensive thought generation capabilities.
2
(a)lo (b) CoT (9 Corse (d) ToT (f) XoT ic ic) 1 thoughts - Unevalt Hl DD âtought Positive thought Policy/Value ' ' Network on) i ©} mative thought
Figure 1: Comparison of XOT versus other prompting paradigms. | 2311.04254#8 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 8,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "We comprehensively evaluate XOT across a diverse range of challenging problem-solving tasks, namely Game of 24, 8-Puzzle, and Pocket Cube. Our experimental results consistently showcase XOTâs superior performance, and its capacity to provide multiple solutions to problems efficiently with just a few LLM calls. These findings establish XOT as an effective thought generation ap- proach, paving the way for new avenues in LLMsâ problem-solving capabilities.\n# 2 BACKGROUND\nThought for LLMs. Addressing complex problems often entails breaking down the overarching ob- jective into multiple intermediary steps. The outcomes or cognitive processes associated with each step are thoughts, which can be expressed as linguistic prompt sequences for LLMs to facilitate problem-solving. Structures of these thought may take various forms, including linear chains, hier- archical trees, or interconnected graphs, depending on how the thoughts are organized to advance towards a solution.\n1We named it âEverything of Thoughtsâ to signify its three comprehensive thought generation capabilities.\n2\n(a)lo (b) CoT (9 Corse (d) ToT (f) XoT ic ic) 1 thoughts - Unevalt Hl DD âtought Positive thought Policy/Value ' ' Network on) i ©} mative thought\nFigure 1: Comparison of XOT versus other prompting paradigms.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 9 | Figure 1: Comparison of XOT versus other prompting paradigms.
Input-Output (IO) Prompting (Fig. 1 (a)). The IO method is the most straightforward approach to instruct LLMs to address a problem without the provision of any intermediate thought processes.
Chain-of-thought (CoT) Wei et al. (2022) (Fig. 1 (b)). CoT decomposes problem-solving into a sequential chain of thoughts, allowing LLMs to approach complex problems step by step.
Self-consistency CoT (CoT-SC) Wang et al. (2023a) (Fig. 1 (c)). CoT-SC employs multiple in- stances of the CoT to generate multiple outputs from LLMs. It selects the the best results from multiple LLM outputs, offering more robust and consistent inference compared to the vanilla CoT.
Tree-of-thought (ToT) Yao et al. (2023) (Fig. 1 (d)). ToT organizes thoughts in a tree-like structure and utilizes search algorithms (e.g., Breadth-First Search, Depth-First Search) to expand the tree in pursuit of an optimal solution. However, thought evaluation in ToT relies on LLMs themselves, necessitating multiple costly and inefficient LLM inference calls. | 2311.04254#9 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 9,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Figure 1: Comparison of XOT versus other prompting paradigms.\nInput-Output (IO) Prompting (Fig. 1 (a)). The IO method is the most straightforward approach to instruct LLMs to address a problem without the provision of any intermediate thought processes.\nChain-of-thought (CoT) Wei et al. (2022) (Fig. 1 (b)). CoT decomposes problem-solving into a sequential chain of thoughts, allowing LLMs to approach complex problems step by step.\nSelf-consistency CoT (CoT-SC) Wang et al. (2023a) (Fig. 1 (c)). CoT-SC employs multiple in- stances of the CoT to generate multiple outputs from LLMs. It selects the the best results from multiple LLM outputs, offering more robust and consistent inference compared to the vanilla CoT.\nTree-of-thought (ToT) Yao et al. (2023) (Fig. 1 (d)). ToT organizes thoughts in a tree-like structure and utilizes search algorithms (e.g., Breadth-First Search, Depth-First Search) to expand the tree in pursuit of an optimal solution. However, thought evaluation in ToT relies on LLMs themselves, necessitating multiple costly and inefficient LLM inference calls.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 10 | Graph-of-thought (GoT) Besta et al. (2023) (Fig. 1 (e)). GoT extends the ToT approach by en- abling the generation of graph-like thought structures through thought aggregation and refinement during intermediate search phases. Although this method permits more flexible thought structures, it still demands multiple LLM inference calls for evaluation, incurring significant computational costs.
# 3 XOT: EVERYTHING OF THOUGHTS
XOT serves as an LLM-MCTS collaborative framework designed to enhance the thought generation process, thereby assisting LLMs in resolving complex problems. It leverages MCTS for proficient and efficient thought exploration while harnessing the capabilities of LLMs to refine and amend the thoughts derived from MCTS. This synergistic interaction creates a mutually beneficial arrangement, ultimately enabling the successful resolution of intricate problems characterized by high levels of performance, efficiency, and flexibility.
3.1 XOT IN A NUTSHELL
We present an overview of the architecture of XOT in Fig. 1 (f). XOT comprises two key compo- nents: (i) a MCTS module guided by policy/value networks; and (iii) an LLM solver for thought revision and inference. The MCTS and policy/value networks need to be trained and then generalize to the inference process. | 2311.04254#10 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 10,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Graph-of-thought (GoT) Besta et al. (2023) (Fig. 1 (e)). GoT extends the ToT approach by en- abling the generation of graph-like thought structures through thought aggregation and refinement during intermediate search phases. Although this method permits more flexible thought structures, it still demands multiple LLM inference calls for evaluation, incurring significant computational costs.\n# 3 XOT: EVERYTHING OF THOUGHTS\nXOT serves as an LLM-MCTS collaborative framework designed to enhance the thought generation process, thereby assisting LLMs in resolving complex problems. It leverages MCTS for proficient and efficient thought exploration while harnessing the capabilities of LLMs to refine and amend the thoughts derived from MCTS. This synergistic interaction creates a mutually beneficial arrangement, ultimately enabling the successful resolution of intricate problems characterized by high levels of performance, efficiency, and flexibility.\n3.1 XOT IN A NUTSHELL\nWe present an overview of the architecture of XOT in Fig. 1 (f). XOT comprises two key compo- nents: (i) a MCTS module guided by policy/value networks; and (iii) an LLM solver for thought revision and inference. The MCTS and policy/value networks need to be trained and then generalize to the inference process.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 11 | During the training phase, MCTS is harnessed to explore potential thought structures for a spe- cific task through simulated scenarios. This process entails the recording of states, values, and the visitation frequencies of thought nodes in each simulation. These recorded data are subsequently employed to iteratively train the policy and value estimation model, enabling it to assimilate domain knowledge and comprehend the world model.
Once trained, the estimated policy and value are utilized to guide the MCTS to systematically search for a thought trajectory provided to aid LLMs in problem-solving. Note that thoughts extracted only play a supporting role, assisting LLMs in gathering knowledge from external sources. These thoughts do not provide LLMs with definitive or error-free answers, as they may contain inaccu- racies or suboptimal solutions. LLMs are responsible for review and refining these thoughts when they seem erroneous or require adjustments. They continue MCTS the search process if needed
3
(a) Select (b) Expand & Evaluate (c) Backpropagation (d) Thought inference Extracted so a a a ee E Ext aN a ace iS g i hl 6 © 6 OB ARS a Tem ee oe oo s A (P,v) = fa : : a : a } Pt = Q J K~ $3 ratatate BR sews 2 Potente JOR ae | 2311.04254#11 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 11,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "During the training phase, MCTS is harnessed to explore potential thought structures for a spe- cific task through simulated scenarios. This process entails the recording of states, values, and the visitation frequencies of thought nodes in each simulation. These recorded data are subsequently employed to iteratively train the policy and value estimation model, enabling it to assimilate domain knowledge and comprehend the world model.\nOnce trained, the estimated policy and value are utilized to guide the MCTS to systematically search for a thought trajectory provided to aid LLMs in problem-solving. Note that thoughts extracted only play a supporting role, assisting LLMs in gathering knowledge from external sources. These thoughts do not provide LLMs with definitive or error-free answers, as they may contain inaccu- racies or suboptimal solutions. LLMs are responsible for review and refining these thoughts when they seem erroneous or require adjustments. They continue MCTS the search process if needed\n3\n(a) Select (b) Expand & Evaluate (c) Backpropagation (d) Thought inference Extracted so a a a ee E Ext aN a ace iS g i hl 6 © 6 OB ARS a Tem ee oe oo s A (P,v) = fa : : a : a } Pt = Q J K~ $3 ratatate BR sews 2 Potente JOR ae",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 12 | Figure 2: An illustration of iterative phases in MCTS for thought searching ((a)-(c)) and thought inference in problem resolution (d).
and eventually formulate the final answers by integrating these external thoughts with their internal knowledge.
3.2 THOUGHT SEARCHING FORMULATION
The fundamental objective of employing the thought generation paradigm for LLMs is to identify the optimal decomposition of a complex problem into several manageable sub-steps. Each sub-step aims to alter the current status of the problem, eventually culminating in the successful resolution of the overarching problem. This approach, as seen in ToT and GoT, hinges on well-defined state tran- sitions and clear final objectives. Consequently, it is natural to conceptualize the thought-searching process as a Markov Decision Process (MDP) Puterman (1990), in which:
⢠State st: Represents the current status of the problem. The initial state s0 corresponds to the original problem, while intermediate states are characterized by either decomposed sub-problems or the results stemming from their resolution.
⢠Action at: Signifies the one-step solution or action associated with tackling a problem, leading to a transition to a new state, by incorporating their outcomes.
⢠Reward r: Reflects the comprehensive evaluation of the solution to the original problem, assess- ing whether it has been effectively resolved through the process of problem decomposition. | 2311.04254#12 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 12,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Figure 2: An illustration of iterative phases in MCTS for thought searching ((a)-(c)) and thought inference in problem resolution (d).\nand eventually formulate the final answers by integrating these external thoughts with their internal knowledge.\n3.2 THOUGHT SEARCHING FORMULATION\nThe fundamental objective of employing the thought generation paradigm for LLMs is to identify the optimal decomposition of a complex problem into several manageable sub-steps. Each sub-step aims to alter the current status of the problem, eventually culminating in the successful resolution of the overarching problem. This approach, as seen in ToT and GoT, hinges on well-defined state tran- sitions and clear final objectives. Consequently, it is natural to conceptualize the thought-searching process as a Markov Decision Process (MDP) Puterman (1990), in which:\n⢠State st: Represents the current status of the problem. The initial state s0 corresponds to the original problem, while intermediate states are characterized by either decomposed sub-problems or the results stemming from their resolution.\n⢠Action at: Signifies the one-step solution or action associated with tackling a problem, leading to a transition to a new state, by incorporating their outcomes.\n⢠Reward r: Reflects the comprehensive evaluation of the solution to the original problem, assess- ing whether it has been effectively resolved through the process of problem decomposition.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 13 | ⢠Reward r: Reflects the comprehensive evaluation of the solution to the original problem, assess- ing whether it has been effectively resolved through the process of problem decomposition.
⢠Thought Ï : A one-step thought is a combination of one-step state and action, i.e., Ï = {s, a}. This formulation naturally encapsulates the process of decomposing a complex problem into multiple sub-tasks, each accompanied by their respective outcomes.
The detailed definitions of state, action, reward and thought for each task are shown in Table 1. The generation of complete thoughts T = {Ï1, · · · , ÏN }, can be construed as the endeavor to discover a thought trajectory to maximize the accumulated reward to address the overall problem.
3.3 THOUGHTS SEARCHING WITH MCTS | 2311.04254#13 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 13,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "⢠Reward r: Reflects the comprehensive evaluation of the solution to the original problem, assess- ing whether it has been effectively resolved through the process of problem decomposition.\n⢠Thought Ï : A one-step thought is a combination of one-step state and action, i.e., Ï = {s, a}. This formulation naturally encapsulates the process of decomposing a complex problem into multiple sub-tasks, each accompanied by their respective outcomes.\nThe detailed definitions of state, action, reward and thought for each task are shown in Table 1. The generation of complete thoughts T = {Ï1, · · · , ÏN }, can be construed as the endeavor to discover a thought trajectory to maximize the accumulated reward to address the overall problem.\n3.3 THOUGHTS SEARCHING WITH MCTS",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 14 | 3.3 THOUGHTS SEARCHING WITH MCTS
The formulation above naturally aligns the thought within LLM as a state-action pair. This approach facilitates the effective exploration of its optimal trajectory using a combination of MCTS and RL. This adheres to an iterative simulation cycle that encompasses three key phases: selection, expansion & evaluation, and backpropagation. It heavily depends on the utilization of neural networks fθ, which simultaneously estimate the value and action probability for a given state st. The aim is to reduce the number of rollouts and accelerate the search process, similar to the approach employed in AlphaGo Zero Silver et al. (2017). We provide a visual representation of an iteration of the MCTS in Fig. 2 (a)-(c) by taking Pocket Cube as an example and detail each process below.
Selection. In the selection phase, the algorithm initiates at the root node and proceeds to choose an action aâ from the available set A(s) for single-step thought generation in the current state s. This process continues until a leaf node within the current tree is reached. The selection is guided by the PUCT algorithm Rosin (2011), aiming to maximize the Upper Confidence Bound (UCB) Garivier
4
& Moulines (2011), as follows: | 2311.04254#14 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 14,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "3.3 THOUGHTS SEARCHING WITH MCTS\nThe formulation above naturally aligns the thought within LLM as a state-action pair. This approach facilitates the effective exploration of its optimal trajectory using a combination of MCTS and RL. This adheres to an iterative simulation cycle that encompasses three key phases: selection, expansion & evaluation, and backpropagation. It heavily depends on the utilization of neural networks fθ, which simultaneously estimate the value and action probability for a given state st. The aim is to reduce the number of rollouts and accelerate the search process, similar to the approach employed in AlphaGo Zero Silver et al. (2017). We provide a visual representation of an iteration of the MCTS in Fig. 2 (a)-(c) by taking Pocket Cube as an example and detail each process below.\nSelection. In the selection phase, the algorithm initiates at the root node and proceeds to choose an action aâ from the available set A(s) for single-step thought generation in the current state s. This process continues until a leaf node within the current tree is reached. The selection is guided by the PUCT algorithm Rosin (2011), aiming to maximize the Upper Confidence Bound (UCB) Garivier\n4\n& Moulines (2011), as follows:",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 15 | 4
& Moulines (2011), as follows:
aâ = arg max aâA(s) Q(s, a) + w · Pθ(s, a) N (s) 1 + N (s, a) . (1)
Here, Q(s, a) denotes the Q-value of a state-action pair (s, a). The term Pθ(s, a) denotes the pre- dicted prior probability of selecting action a given the state s obtained from a neural network fθ, and N (s, a) represents the count of times action a has been chosen in state s. The parameter w con- trols the trade-off between exploration and exploitation. The selection process will continue until an unexplored node is encountered. | 2311.04254#15 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 15,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4\n& Moulines (2011), as follows:\naâ = arg max aâA(s) Q(s, a) + w · Pθ(s, a) N (s) 1 + N (s, a) . (1)\nHere, Q(s, a) denotes the Q-value of a state-action pair (s, a). The term Pθ(s, a) denotes the pre- dicted prior probability of selecting action a given the state s obtained from a neural network fθ, and N (s, a) represents the count of times action a has been chosen in state s. The parameter w con- trols the trade-off between exploration and exploitation. The selection process will continue until an unexplored node is encountered.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 16 | Evaluation and Expansion. Upon reaching a previously unselected leaf node, we expand to the state s for the next step for new thought exploration. This expansion involves the evaluation of its value and action probability on the state, which are modeled by neural networks parameterized by θ, i.e., (Pθ(s), vθ(s)) = fθ(s). Here Pθ(s) is the prior probabilities for all actions on s, and vθ(s) denotes its predicted state value. These two values are retained and stored for backup purposes, and state s is masked as âvisitedâ.
Backpropagation. Following the expansion of a leaf node in the above phases, which could be either an unexplored or terminal state, the algorithm proceeds to update all the Q(s, a) values via backpropagation. For unexplored nodes, this update involves computing the mean of its estimated value vθ, while for terminated nodes, itâs based on the true reward r. These updates occur as infor- mation is backpropagated along the trajectory to subsequent nodes. Additionally, the visit count for each state-action pair is also incremented as follows: N (s, a) = N (s, a) + 1. | 2311.04254#16 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 16,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Evaluation and Expansion. Upon reaching a previously unselected leaf node, we expand to the state s for the next step for new thought exploration. This expansion involves the evaluation of its value and action probability on the state, which are modeled by neural networks parameterized by θ, i.e., (Pθ(s), vθ(s)) = fθ(s). Here Pθ(s) is the prior probabilities for all actions on s, and vθ(s) denotes its predicted state value. These two values are retained and stored for backup purposes, and state s is masked as âvisitedâ.\nBackpropagation. Following the expansion of a leaf node in the above phases, which could be either an unexplored or terminal state, the algorithm proceeds to update all the Q(s, a) values via backpropagation. For unexplored nodes, this update involves computing the mean of its estimated value vθ, while for terminated nodes, itâs based on the true reward r. These updates occur as infor- mation is backpropagated along the trajectory to subsequent nodes. Additionally, the visit count for each state-action pair is also incremented as follows: N (s, a) = N (s, a) + 1.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 17 | A simulation is completed after a sequence of selection, evaluation, expansion, and backpropagation steps. After conducting multiple simulations, we proceed to the next step by selecting an action at state s using a probability distribution defined as εa â N (s, a)1/γ, where γ is a temperature constant that regulates the level of exploration.
Policy and Value Networks Training. The simulations described above allow us to compile a dataset for each sample state s containing (s, ε(s), v(s)), where ε(s) = {εa | a â A(s)}, and v(s) represents the ground truth value obtained by accumulating rewards along the trajectory starting from state s. Subsequently, we can train a combined policy and value network fθ to minimize the discrepancy between the predicted value vθ(s) and the actual value v(s), while also maximizing the alignment between the action probabilities produced by the neural network Pθ(s) and the search probabilities ε(s). This can be achieved by minimizing the following loss function:
L = (v(s) â vθ(s))2 + ε(s)T log Pθ(s)). This training iterates alongside the simulation process to continually enhance the performance of fθ, resulting in progressive improvements in thought searching capabilities.
3.4 THOUGHT INFERENCE WITH MCTS | 2311.04254#17 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 17,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "A simulation is completed after a sequence of selection, evaluation, expansion, and backpropagation steps. After conducting multiple simulations, we proceed to the next step by selecting an action at state s using a probability distribution defined as εa â N (s, a)1/γ, where γ is a temperature constant that regulates the level of exploration.\nPolicy and Value Networks Training. The simulations described above allow us to compile a dataset for each sample state s containing (s, ε(s), v(s)), where ε(s) = {εa | a â A(s)}, and v(s) represents the ground truth value obtained by accumulating rewards along the trajectory starting from state s. Subsequently, we can train a combined policy and value network fθ to minimize the discrepancy between the predicted value vθ(s) and the actual value v(s), while also maximizing the alignment between the action probabilities produced by the neural network Pθ(s) and the search probabilities ε(s). This can be achieved by minimizing the following loss function:\nL = (v(s) â vθ(s))2 + ε(s)T log Pθ(s)). This training iterates alongside the simulation process to continually enhance the performance of fθ, resulting in progressive improvements in thought searching capabilities.\n3.4 THOUGHT INFERENCE WITH MCTS",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 18 | 3.4 THOUGHT INFERENCE WITH MCTS
Once trained, we utilize the fθ to guide the MCTS in generating a thought for a new problem, which assists the LLM in solving it. Specifically, MCTS is utilized to perform K simulations aimed at thought searching and problem-solving, as illustrated in Fig.2 (d). In each simulation, fθ is em- ployed to guide the MCTS in its search for a thought trajectory. Throughout the training process, fθ incorporates external information related to the state and action quality. This information helps LLMs understand the world model, enhancing their long-term reasoning and planning abilities, which are areas they may not excel in Stechly et al. (2023); Valmeekam et al. (2023), thereby ensur- ing the performance of thought generation. Once the simulation concludes, we record the visiting count N (s, a) and the thought trajectory is obtained based on the number of solutions required:
⢠Single solution. starting from each state s, the action with the highest visiting count N (s, a) is selected.
⢠Multiple solution. we sample M thought trajectories following the probability distribution εa â N (s, a) and remove duplicates. | 2311.04254#18 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 18,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "3.4 THOUGHT INFERENCE WITH MCTS\nOnce trained, we utilize the fθ to guide the MCTS in generating a thought for a new problem, which assists the LLM in solving it. Specifically, MCTS is utilized to perform K simulations aimed at thought searching and problem-solving, as illustrated in Fig.2 (d). In each simulation, fθ is em- ployed to guide the MCTS in its search for a thought trajectory. Throughout the training process, fθ incorporates external information related to the state and action quality. This information helps LLMs understand the world model, enhancing their long-term reasoning and planning abilities, which are areas they may not excel in Stechly et al. (2023); Valmeekam et al. (2023), thereby ensur- ing the performance of thought generation. Once the simulation concludes, we record the visiting count N (s, a) and the thought trajectory is obtained based on the number of solutions required:\n⢠Single solution. starting from each state s, the action with the highest visiting count N (s, a) is selected.\n⢠Multiple solution. we sample M thought trajectories following the probability distribution εa â N (s, a) and remove duplicates.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 19 | ⢠Multiple solution. we sample M thought trajectories following the probability distribution εa â N (s, a) and remove duplicates.
This results in one or multiple thought trajectories T â that consist of a sequence of state-action pairs for problem-solving. The trajectories for multi-solution problems may intertwine and converge at
5
MCTS LLM LLM _ââ Identified Extract error state Extract Extracted Simulations hought Additional L Revised thoughts Simulations thoughts (ference)
Figure 3: An illustration of thought revision process in XOT.
the same goal state, resulting in a graph-like thought structure. This demonstrates that XOT is capable of generating thought structures with flexibility. These trajectories are then transformed into text sequences that are concatenated to form a prompt sequence provided to LLMs. Note that the thought trajectory is concatenated into a single prompt, even in the case of problems with multiple solutions. Therefore, we only require a single LLM inference call at this stage. Given that the fθ network is relatively lightweight, this ensures the efficiency of XOT. | 2311.04254#19 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 19,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "⢠Multiple solution. we sample M thought trajectories following the probability distribution εa â N (s, a) and remove duplicates.\nThis results in one or multiple thought trajectories T â that consist of a sequence of state-action pairs for problem-solving. The trajectories for multi-solution problems may intertwine and converge at\n5\nMCTS LLM LLM _ââ Identified Extract error state Extract Extracted Simulations hought Additional L Revised thoughts Simulations thoughts (ference)\nFigure 3: An illustration of thought revision process in XOT.\nthe same goal state, resulting in a graph-like thought structure. This demonstrates that XOT is capable of generating thought structures with flexibility. These trajectories are then transformed into text sequences that are concatenated to form a prompt sequence provided to LLMs. Note that the thought trajectory is concatenated into a single prompt, even in the case of problems with multiple solutions. Therefore, we only require a single LLM inference call at this stage. Given that the fθ network is relatively lightweight, this ensures the efficiency of XOT.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 20 | Thought Revision. It is important to acknowledge that that MCTS may not always provide the globally optimal thought trajectory to directly solve the problem flawlessly. Therefore, the thoughts extracted from MCTS serve as a reference thinking process for the problem, aiding LLMs in a sup- portive capacity. The LLMs will leverage their internal knowledge to review the extracted thought, identify errors in the thought trajectory, and then ground its knowledge in collaboration with the MCTS to revise and refine the thought. | 2311.04254#20 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 20,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Thought Revision. It is important to acknowledge that that MCTS may not always provide the globally optimal thought trajectory to directly solve the problem flawlessly. Therefore, the thoughts extracted from MCTS serve as a reference thinking process for the problem, aiding LLMs in a sup- portive capacity. The LLMs will leverage their internal knowledge to review the extracted thought, identify errors in the thought trajectory, and then ground its knowledge in collaboration with the MCTS to revise and refine the thought.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 21 | The revision process is iterative in nature, as shown in Fig. 3. Initially, upon obtaining the extracted thought, we instruct the LLM to detect any errors in the thought generated by MCTS using its in- ternal knowledge. If the LLM identifies an error, it results in an error state denoted as se within the thought. If no error is found, the thought remains unchanged. Starting from the parent state of se, MCTS conducts an additional set of L simulations, ultimately yielding a revised thought for the LLM. In scenarios involving multiple solutions, each solution undergoes this process individually. Upon the completion of the revision, we supply the LLMs with the revised thoughts for problem- solving. The revision process can be repeated several times to enhance the reliability of the answer. This collaborative MCTS-LLM framework nurtures a mutually beneficial process for both compo- nents, ultimately contributing to the overall performance of problem-solving. Since LLMs are solely utilized for identifying errors during the revision process with only one call, the efficiency of XOT is effectively maintained. | 2311.04254#21 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 21,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "The revision process is iterative in nature, as shown in Fig. 3. Initially, upon obtaining the extracted thought, we instruct the LLM to detect any errors in the thought generated by MCTS using its in- ternal knowledge. If the LLM identifies an error, it results in an error state denoted as se within the thought. If no error is found, the thought remains unchanged. Starting from the parent state of se, MCTS conducts an additional set of L simulations, ultimately yielding a revised thought for the LLM. In scenarios involving multiple solutions, each solution undergoes this process individually. Upon the completion of the revision, we supply the LLMs with the revised thoughts for problem- solving. The revision process can be repeated several times to enhance the reliability of the answer. This collaborative MCTS-LLM framework nurtures a mutually beneficial process for both compo- nents, ultimately contributing to the overall performance of problem-solving. Since LLMs are solely utilized for identifying errors during the revision process with only one call, the efficiency of XOT is effectively maintained.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 22 | The collaborative revision framework harnesses the strengths of both MCTS and LLMs. MCTS efficiently and flexibly generates candidate thoughts for LLMs through simulations, while LLMs use their internal knowledge to revise and ground these thoughts within the MCTS framework, effectively turning MCTS into a world model for LLMs. This process ensures the generation of high-quality thoughts for problem-solving.
# 4 EXPERIMENT
We conduct an extensive evaluation of our XOT approach2 in comparison to several baseline meth- ods across three challenging tasks: the Game of 24, the 8-Puzzle (with a 3 Ã 3 grid), and the 2 Ã 2 Pocket Cube. An overview of these tasks is provided in Table 2. These tasks are characterized by their complexity, requiring multiple steps for completion and potentially having multiple solutions. To assess the effectiveness of our proposed XOT, we compare it against IO, CoT, CoT-SC, ToT, and GoT methodologies. We employ both GPT-3.5 Ouyang et al. (2022) and GPT-4 OpenAI (2023) for these evaluations. Note that temperature and top p are set to 0.0 for all LLM invoked.
2Code and dataset to reproduce this work will be shared in the near future, following compliance with the affiliation policy.
6
Table 2: An overview of tasks employed in this study. | 2311.04254#22 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 22,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "The collaborative revision framework harnesses the strengths of both MCTS and LLMs. MCTS efficiently and flexibly generates candidate thoughts for LLMs through simulations, while LLMs use their internal knowledge to revise and ground these thoughts within the MCTS framework, effectively turning MCTS into a world model for LLMs. This process ensures the generation of high-quality thoughts for problem-solving.\n# 4 EXPERIMENT\nWe conduct an extensive evaluation of our XOT approach2 in comparison to several baseline meth- ods across three challenging tasks: the Game of 24, the 8-Puzzle (with a 3 Ã 3 grid), and the 2 Ã 2 Pocket Cube. An overview of these tasks is provided in Table 2. These tasks are characterized by their complexity, requiring multiple steps for completion and potentially having multiple solutions. To assess the effectiveness of our proposed XOT, we compare it against IO, CoT, CoT-SC, ToT, and GoT methodologies. We employ both GPT-3.5 Ouyang et al. (2022) and GPT-4 OpenAI (2023) for these evaluations. Note that temperature and top p are set to 0.0 for all LLM invoked.\n2Code and dataset to reproduce this work will be shared in the near future, following compliance with the affiliation policy.\n6\nTable 2: An overview of tasks employed in this study.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 23 | Objective Input Output Thought State Action Game of 24 Use four numbers on playing cards to make the number 24 through +, â, Ã, or ÷. 4 numbers ranging from 1 to 13, e.g., (4, 6, 10, 10). An equation to reach 24, e.g., 4 à 6 + 10 â 10 = 24. 3 intermediate equations. The remaining 1-4 numbers. Picking two number and a operation to compose an equation. 8-Puzzle Rearrange the tiles in the 3 à 3 puzzle from an scrambled state to a goal state . A scrambled 3 à 3 digital puzzle, e.g., . The slide sequence of the â-â tile, e.g., (Up, Down, Left, Right · · · ). The step-by-step sliding, and the puzzle state after the move. The current number layout of the puzzle. The one-step moving action of the â-â tile. Pocket Cube Rotating the faces of a 2 à 2 pocket cube until each face of the cube is a uniform color A scrambled 2 à 2 . pocket cube, e.g., . Colors represented as numbers for LLMs. The rotation move sequence of the cube, e.g., (F, R2, Uâ · · · ). The | 2311.04254#23 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 23,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Objective Input Output Thought State Action Game of 24 Use four numbers on playing cards to make the number 24 through +, â, Ã, or ÷. 4 numbers ranging from 1 to 13, e.g., (4, 6, 10, 10). An equation to reach 24, e.g., 4 à 6 + 10 â 10 = 24. 3 intermediate equations. The remaining 1-4 numbers. Picking two number and a operation to compose an equation. 8-Puzzle Rearrange the tiles in the 3 à 3 puzzle from an scrambled state to a goal state . A scrambled 3 à 3 digital puzzle, e.g., . The slide sequence of the â-â tile, e.g., (Up, Down, Left, Right · · · ). The step-by-step sliding, and the puzzle state after the move. The current number layout of the puzzle. The one-step moving action of the â-â tile. Pocket Cube Rotating the faces of a 2 à 2 pocket cube until each face of the cube is a uniform color A scrambled 2 à 2 . pocket cube, e.g., . Colors represented as numbers for LLMs. The rotation move sequence of the cube, e.g., (F, R2, Uâ · · · ). The",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 24 | . Colors represented as numbers for LLMs. The rotation move sequence of the cube, e.g., (F, R2, Uâ · · · ). The step-by-step rotation, and the cube state after the move. Colors of each face of the pocket cube. The one-step rotation action of cube. Reward 1 if the number of the final number is equal to 24 otherwise -1. The negative minimum step on solving the current puzzle state toward the goal state. The negative minimum moving step on solving current cube state toward the goal state. | 2311.04254#24 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 24,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": ". Colors represented as numbers for LLMs. The rotation move sequence of the cube, e.g., (F, R2, Uâ · · · ). The step-by-step rotation, and the cube state after the move. Colors of each face of the pocket cube. The one-step rotation action of cube. Reward 1 if the number of the final number is equal to 24 otherwise -1. The negative minimum step on solving the current puzzle state toward the goal state. The negative minimum moving step on solving current cube state toward the goal state.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 25 | Policy/Value Networks Configurations. The policy and value networks in our model utilize a shared multi-layer perceptron (MLP) architecture with two layers and hidden units arranged as (128, 256). Two heads connected to the MLP are responsible for predicting vθ(s) and Pθ(s) separately. This design results in a considerably smaller model compared to LLM, making it much more ef- ficient. We train this model through three iterations, with each iteration comprising 10 self-play episodes for MCTS.
Evaluation Metric. For each task, we assess the accuracy of each approach on the test set. Addi- tionally, we track the number of LLM invocations required for all approaches to solve a problem, as well as the number of times fθ is invoked in the case of XOT. Itâs important to note that fθ is a considerably smaller model compared to LLMs. In the context of multi-solution scenarios, ac- curacy is computed as the percentage of problems for which any of the answers provided by each approach is correct. Multi-solution Accuracy (MultiAcc) is calculated as the average percentage of correctness across all solutions offered. Furthermore, we capture the total count of distinct solutions provided by each approach, regardless of their correctness, represented as #Sol. Note that we set the maximum solution number to 3 for all problems in multi-solution scenarios. | 2311.04254#25 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 25,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Policy/Value Networks Configurations. The policy and value networks in our model utilize a shared multi-layer perceptron (MLP) architecture with two layers and hidden units arranged as (128, 256). Two heads connected to the MLP are responsible for predicting vθ(s) and Pθ(s) separately. This design results in a considerably smaller model compared to LLM, making it much more ef- ficient. We train this model through three iterations, with each iteration comprising 10 self-play episodes for MCTS.\nEvaluation Metric. For each task, we assess the accuracy of each approach on the test set. Addi- tionally, we track the number of LLM invocations required for all approaches to solve a problem, as well as the number of times fθ is invoked in the case of XOT. Itâs important to note that fθ is a considerably smaller model compared to LLMs. In the context of multi-solution scenarios, ac- curacy is computed as the percentage of problems for which any of the answers provided by each approach is correct. Multi-solution Accuracy (MultiAcc) is calculated as the average percentage of correctness across all solutions offered. Furthermore, we capture the total count of distinct solutions provided by each approach, regardless of their correctness, represented as #Sol. Note that we set the maximum solution number to 3 for all problems in multi-solution scenarios.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 26 | 4.1 GAME OF 24
The Game of 24 presents a arithmetic challenge wherein the goal is to employ four numbers within the range of 1 to 13, in conjunction with basic arithmetic operations, (i.e., +, â, Ã, ÷), to attain a final result of 24. This game may possess multiple valid solutions.
# 4.1.1 TASK SETUP
We collect a dataset from 4nu, comprising 1,362 games ranked by human solving time, spanning a range of difficulty levels from easy to hard. For our testing phase, we randomly selected 137 games, ensuring coverage of various difficulty intervals. The remaining 1,225 problems were used to train the policy/value networks with MCTS. In the context of this task, as outlined in Table 1, the thoughts refer to the three intermediate equations, while the state encompasses the available numbers (ranging
7
Table 3: Performance comparison on Game of 24. | 2311.04254#26 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 26,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.1 GAME OF 24\nThe Game of 24 presents a arithmetic challenge wherein the goal is to employ four numbers within the range of 1 to 13, in conjunction with basic arithmetic operations, (i.e., +, â, Ã, ÷), to attain a final result of 24. This game may possess multiple valid solutions.\n# 4.1.1 TASK SETUP\nWe collect a dataset from 4nu, comprising 1,362 games ranked by human solving time, spanning a range of difficulty levels from easy to hard. For our testing phase, we randomly selected 137 games, ensuring coverage of various difficulty intervals. The remaining 1,225 problems were used to train the policy/value networks with MCTS. In the context of this task, as outlined in Table 1, the thoughts refer to the three intermediate equations, while the state encompasses the available numbers (ranging\n7\nTable 3: Performance comparison on Game of 24.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 27 | 7
Table 3: Performance comparison on Game of 24.
Model IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 6.57 2.19 2.19 5.84 10.22 2.92 61.31 79.56 1.00 1.00 10.00 22.11 43.96 7.00 1.00 1.39 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 68.73 92.15 10.22 4.38 4.38 34.31 60.58 10.95 63.50 74.45 1.00 1.00 10.00 23.50 39.83 7.00 1.00 1.38 fθ invoked - - - - - - 68.69 88.20
from 1 to 4) for creating the equations. Actions involve the selection of two numbers and an operator to form an equation, and the reward is set to 1 if the final equation is both valid and results in the number 24, utilizing each of the input numbers exactly once, otherwise it is set to -1. Performance is measured by calculating the success rate across the 137 test games. | 2311.04254#27 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 27,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "7\nTable 3: Performance comparison on Game of 24.\nModel IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 6.57 2.19 2.19 5.84 10.22 2.92 61.31 79.56 1.00 1.00 10.00 22.11 43.96 7.00 1.00 1.39 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 68.73 92.15 10.22 4.38 4.38 34.31 60.58 10.95 63.50 74.45 1.00 1.00 10.00 23.50 39.83 7.00 1.00 1.38 fθ invoked - - - - - - 68.69 88.20\nfrom 1 to 4) for creating the equations. Actions involve the selection of two numbers and an operator to form an equation, and the reward is set to 1 if the final equation is both valid and results in the number 24, utilizing each of the input numbers exactly once, otherwise it is set to -1. Performance is measured by calculating the success rate across the 137 test games.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 28 | 4.1.2 BASELINES & XOT SETUP
The IO prompt is supported by five in-context examples. In the case of CoT, we augment each input-output pair by including three intermediate equations. As for ToT, we solicit one-step thought candidates from the LLM at each step, subsequently instructing the LLM to categorize each thought candidate for intermediate selection. For experimental comparison, we conduct experiments on both the top-1 candidate (with b=1) and the top-3 candidates (with b=3) being retained, where b indicates the branches retained for exploration at each step. For GoT, we employ LLM to generate one-step thought candidates in the same manner as ToT, then we direct the LLM to select the top-1 thought from all candidates for merging the thoughts. We also examine a CoT-SC baseline, which derives the majority output from 10 CoT samples. For XOT, we perform 200 simulations for each action taken, and this count is increased to 500 during the thought revision process. | 2311.04254#28 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 28,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.1.2 BASELINES & XOT SETUP\nThe IO prompt is supported by five in-context examples. In the case of CoT, we augment each input-output pair by including three intermediate equations. As for ToT, we solicit one-step thought candidates from the LLM at each step, subsequently instructing the LLM to categorize each thought candidate for intermediate selection. For experimental comparison, we conduct experiments on both the top-1 candidate (with b=1) and the top-3 candidates (with b=3) being retained, where b indicates the branches retained for exploration at each step. For GoT, we employ LLM to generate one-step thought candidates in the same manner as ToT, then we direct the LLM to select the top-1 thought from all candidates for merging the thoughts. We also examine a CoT-SC baseline, which derives the majority output from 10 CoT samples. For XOT, we perform 200 simulations for each action taken, and this count is increased to 500 during the thought revision process.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 29 | In the multi-solution scenario, the IO, CoT, and CoT-SC prompts each include 5 examples, with each problem having 1 to 3 different solutions. For ToT, the top-3 candidates (with b=3) at the final step are considered as different solutions. Rather than keeping only the top-1 thought, GoT is instructed to select between 1 to 3 thoughts from all candidates at each step to generate a wider range of solutions. As for XOT, after performing simulations on MCTS, we sample 500 thought trajectories as for exploration and remove deplicates. The top-3 thoughts with the highest counts are preserved.
4.1.3 RESULTS | 2311.04254#29 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 29,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "In the multi-solution scenario, the IO, CoT, and CoT-SC prompts each include 5 examples, with each problem having 1 to 3 different solutions. For ToT, the top-3 candidates (with b=3) at the final step are considered as different solutions. Rather than keeping only the top-1 thought, GoT is instructed to select between 1 to 3 thoughts from all candidates at each step to generate a wider range of solutions. As for XOT, after performing simulations on MCTS, we sample 500 thought trajectories as for exploration and remove deplicates. The top-3 thoughts with the highest counts are preserved.\n4.1.3 RESULTS",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 30 | 4.1.3 RESULTS
Table 3 displays the overall performance of all methods on this task. Notably, XOT consistently outperforms other baselines on both GPT-3.5 and GPT-4, achieving an accuracy of 61.31% and 63.50% respectively, without revision. However, after the revision process, XOTâs accuracy sub- stantially improves to 79.56% and 74.45% for GPT-3.5 and GPT-4 respectively. This underscores the impressive performance of XOT, and demonstrates that the revision process significantly en- hances performance, with only a limited increase in the utilization of LLM and fθ. Interestingly, the revision process in XOT mitigates the performance gap attributable to the modeling ability in this task. As we observe that XOT with GPT-3.5 achieves higher accuracy after revision compared to GPT-4. | 2311.04254#30 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 30,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.1.3 RESULTS\nTable 3 displays the overall performance of all methods on this task. Notably, XOT consistently outperforms other baselines on both GPT-3.5 and GPT-4, achieving an accuracy of 61.31% and 63.50% respectively, without revision. However, after the revision process, XOTâs accuracy sub- stantially improves to 79.56% and 74.45% for GPT-3.5 and GPT-4 respectively. This underscores the impressive performance of XOT, and demonstrates that the revision process significantly en- hances performance, with only a limited increase in the utilization of LLM and fθ. Interestingly, the revision process in XOT mitigates the performance gap attributable to the modeling ability in this task. As we observe that XOT with GPT-3.5 achieves higher accuracy after revision compared to GPT-4.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 31 | On the other hand, the best-performing baseline, ToT (b=3) on GPT-4, attains an accuracy of 60.58%. However, it demands a substantial number of LLM invocations (39.83), which results in inefficiency. In contrast, XOT exhibits a significant advantage in terms of average LLM invocation time. It requires only a single LLM inference without revision and less than 1.4 calls with revision. Although XOT requires some inference calls for fθ, the model is significantly less complex than LLM, making it a much more efficient approach.
Table 4 presents the performance of GPT-3.5 and GPT-4 models across different methods in the multi-solution scenario. Overall, XOT remains the best-performing approach in terms of accuracy and MultiAcc, significantly outperforming other baselines. Its GPT-4 version can even achieve over
8
Table 4: Performance comparison on Game of 24 in the multi-solution scenario. | 2311.04254#31 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 31,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "On the other hand, the best-performing baseline, ToT (b=3) on GPT-4, attains an accuracy of 60.58%. However, it demands a substantial number of LLM invocations (39.83), which results in inefficiency. In contrast, XOT exhibits a significant advantage in terms of average LLM invocation time. It requires only a single LLM inference without revision and less than 1.4 calls with revision. Although XOT requires some inference calls for fθ, the model is significantly less complex than LLM, making it a much more efficient approach.\nTable 4 presents the performance of GPT-3.5 and GPT-4 models across different methods in the multi-solution scenario. Overall, XOT remains the best-performing approach in terms of accuracy and MultiAcc, significantly outperforming other baselines. Its GPT-4 version can even achieve over\n8\nTable 4: Performance comparison on Game of 24 in the multi-solution scenario.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 32 | 8
Table 4: Performance comparison on Game of 24 in the multi-solution scenario.
Model IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 14.6 3.65 5.11 10.22 8.76 72.99 85.40 4.87 1.22 1.70 3.41 8.03 39.90 62.90 GPT-3.5 #Sol 2.88 2.77 2.76 2.99 1.93 2.89 2.29 LLM invoked 1.00 1.00 10.00 43.96 7.00 1.00 3.51 fθ invoked Acc. MultiAcc - - - - - 95.66 116.34 21.17 20.44 18.98 60.58 13.14 72.99 90.51 8.27 7.79 8.03 39.90 10.46 60.54 76.25 GPT-4 #Sol 2.99 2.94 2.99 2.78 1.39 2.55 2.36 LLM invoked 1.00 1.00 10.00 39.83 7.00 1.00 2.31 fθ invoked - - - - - 95.66 109.64 | 2311.04254#32 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 32,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "8\nTable 4: Performance comparison on Game of 24 in the multi-solution scenario.\nModel IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 14.6 3.65 5.11 10.22 8.76 72.99 85.40 4.87 1.22 1.70 3.41 8.03 39.90 62.90 GPT-3.5 #Sol 2.88 2.77 2.76 2.99 1.93 2.89 2.29 LLM invoked 1.00 1.00 10.00 43.96 7.00 1.00 3.51 fθ invoked Acc. MultiAcc - - - - - 95.66 116.34 21.17 20.44 18.98 60.58 13.14 72.99 90.51 8.27 7.79 8.03 39.90 10.46 60.54 76.25 GPT-4 #Sol 2.99 2.94 2.99 2.78 1.39 2.55 2.36 LLM invoked 1.00 1.00 10.00 39.83 7.00 1.00 2.31 fθ invoked - - - - - 95.66 109.64",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 33 | 90% accuracy. Although XOT does not generate the most number of answers compared to other baselines, it generates more accurate answers, as its MultiAcc significantly outperforms other ap- proaches. Notably, generating multiple solutions does not significantly increase XOTâs complexity, as it only requires 2.31 LLM calls with GPT-4 and around 100 calls for a smaller fθ, making it remain efficient. Overall, the remarkable performance of XOT in the multi-solution scenario demonstrates its ability to generate complex thoughts, making it a flexible approach.
4.2 8-PUZZLE
The 8-Puzzle is a classic sliding puzzle game that consists of a 3 Ã 3 grid with eight numbered tiles and one empty space denoted as â-â. Its objective is to rearrange the tiles from a given initial configuration into a target configuration. The maximum number of steps necessary for the optimal solution of the 8-Puzzle is 31. This problem falls within the category of NP-complete problems Ratner & Warmuth (1986) and may have multiple solutions.
4.2.1 TASK SETUP | 2311.04254#33 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 33,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "90% accuracy. Although XOT does not generate the most number of answers compared to other baselines, it generates more accurate answers, as its MultiAcc significantly outperforms other ap- proaches. Notably, generating multiple solutions does not significantly increase XOTâs complexity, as it only requires 2.31 LLM calls with GPT-4 and around 100 calls for a smaller fθ, making it remain efficient. Overall, the remarkable performance of XOT in the multi-solution scenario demonstrates its ability to generate complex thoughts, making it a flexible approach.\n4.2 8-PUZZLE\nThe 8-Puzzle is a classic sliding puzzle game that consists of a 3 à 3 grid with eight numbered tiles and one empty space denoted as â-â. Its objective is to rearrange the tiles from a given initial configuration into a target configuration. The maximum number of steps necessary for the optimal solution of the 8-Puzzle is 31. This problem falls within the category of NP-complete problems Ratner & Warmuth (1986) and may have multiple solutions.\n4.2.1 TASK SETUP",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 34 | 4.2.1 TASK SETUP
We randomly generated 419 solvable 8-puzzle problems, with 300 instances allocated for training and 119 instances for testing. All generated problems are solvable within 9 steps. The action space encompasses four directions: [Up, Down, Left, Right]. Note that the legal action space for each problem state may vary due to the dynamic position of the empty space. As shown in Table 1, the thoughts refer to the step-by-step move, and the puzzle state after the move.
4.2.2 BASELINES & XOT SETUP
The IO prompt is extended with three in-context examples. In the CoT approach, each input-output pair is enriched by incorporating intermediate legal action sets, the current action, and the current state. In ToT, at each stage, a set of one-step thought candidates are derived from the LLM, from the current set of legal actions. We impose a maximum step limit of 9 since all generated problems can be solved within this range. The 8-puzzleâs rules are conveyed through a system message, including detailed explanations of each actionâs execution. Similarly, we perform 20 simulations for each action taken with XOT, and increase this number to 50 for thought revision processes. | 2311.04254#34 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 34,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.2.1 TASK SETUP\nWe randomly generated 419 solvable 8-puzzle problems, with 300 instances allocated for training and 119 instances for testing. All generated problems are solvable within 9 steps. The action space encompasses four directions: [Up, Down, Left, Right]. Note that the legal action space for each problem state may vary due to the dynamic position of the empty space. As shown in Table 1, the thoughts refer to the step-by-step move, and the puzzle state after the move.\n4.2.2 BASELINES & XOT SETUP\nThe IO prompt is extended with three in-context examples. In the CoT approach, each input-output pair is enriched by incorporating intermediate legal action sets, the current action, and the current state. In ToT, at each stage, a set of one-step thought candidates are derived from the LLM, from the current set of legal actions. We impose a maximum step limit of 9 since all generated problems can be solved within this range. The 8-puzzleâs rules are conveyed through a system message, including detailed explanations of each actionâs execution. Similarly, we perform 20 simulations for each action taken with XOT, and increase this number to 50 for thought revision processes.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 35 | In the multi-solution scenario, all of the IO, CoT, and CoT-SC prompts consist of four examples. Each problem is presented with one to three distinct solutions. For ToT (b=3) and GoT (k=3), the maximum number of steps is increased to 12, as correct solutions may not always be optimal and could exceed 9 steps. In the case of XOT, after conducting simulations with MCTS, we sample 50 thought trajectories for exploration and select the top-3 thoughts with the highest counts.
4.2.3 RESULTS
The inherent spatial complexity of the 8-Puzzle, the need for long-term planning, and the presence of invalid actions create a significant challenge for LLMs, which rely solely on textual data as input. This challenge is starkly evident in the poor performance of the baselines on both GPT-3.5, where its IO prompting achieve a mere 0% success rate. XOT successfully addresses this issue by supplying thoughts acquired from MCTS, thereby infusing external knowledge into the problem-solving pro- cess. This augmentation empowers LLMs to tackle problems that were previously insurmountable. In summary, when using GPT-4, XOT achieves an accuracy of 50.42% without revision and 93.2%
9
# Table 5: Performance comparison on 8-Puzzle. | 2311.04254#35 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 35,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "In the multi-solution scenario, all of the IO, CoT, and CoT-SC prompts consist of four examples. Each problem is presented with one to three distinct solutions. For ToT (b=3) and GoT (k=3), the maximum number of steps is increased to 12, as correct solutions may not always be optimal and could exceed 9 steps. In the case of XOT, after conducting simulations with MCTS, we sample 50 thought trajectories for exploration and select the top-3 thoughts with the highest counts.\n4.2.3 RESULTS\nThe inherent spatial complexity of the 8-Puzzle, the need for long-term planning, and the presence of invalid actions create a significant challenge for LLMs, which rely solely on textual data as input. This challenge is starkly evident in the poor performance of the baselines on both GPT-3.5, where its IO prompting achieve a mere 0% success rate. XOT successfully addresses this issue by supplying thoughts acquired from MCTS, thereby infusing external knowledge into the problem-solving pro- cess. This augmentation empowers LLMs to tackle problems that were previously insurmountable. In summary, when using GPT-4, XOT achieves an accuracy of 50.42% without revision and 93.2%\n9\n# Table 5: Performance comparison on 8-Puzzle.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 36 | 9
# Table 5: Performance comparison on 8-Puzzle.
Model IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 0.00 0.00 0.84 5.88 6.72 3.36 49.58 59.66 1.00 1.00 10.00 31.76 55.86 19.00 1.00 1.50 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 36.64 41.09 1.68 7.56 8.40 3.36 13.45 3.36 51.26 93.28 1.00 1.00 10.00 27.49 54.13 19.00 1.00 1.48 fθ invoked - - - - - - 36.25 55.66
Table 6: Performance comparison on 8-Puzzle in the multi-solution scenario. | 2311.04254#36 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 36,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "9\n# Table 5: Performance comparison on 8-Puzzle.\nModel IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 0.00 0.00 0.84 5.88 6.72 3.36 49.58 59.66 1.00 1.00 10.00 31.76 55.86 19.00 1.00 1.50 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 36.64 41.09 1.68 7.56 8.40 3.36 13.45 3.36 51.26 93.28 1.00 1.00 10.00 27.49 54.13 19.00 1.00 1.48 fθ invoked - - - - - - 36.25 55.66\nTable 6: Performance comparison on 8-Puzzle in the multi-solution scenario.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 37 | Table 6: Performance comparison on 8-Puzzle in the multi-solution scenario.
Model IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 0.00 2.52 2.52 6.72 6.72 36.97 52.10 0.00 1.43 1.54 2.52 3.36 21.15 27.45 GPT-3.5 #Sol 2.47 2.05 1.90 2.98 2.96 2.87 2.85 LLM invoked 1.00 1.00 10.00 55.86 24.18 1.00 4.19 fθ invoked - - - - - 36.25 52.06 Acc. MultiAcc 2.52 10.92 11.76 13.45 20.17 50.42 82.35 0.84 7.84 6.58 5.60 16.61 29.13 76.33 GPT-4 #Sol 2.97 1.21 2.08 2.97 2.70 2.97 1.52 LLM invoked 1.00 1.00 10.00 54.13 22.76 1.00 4.30 fθ invoked - - - - - 36.25 66.66 | 2311.04254#37 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 37,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Table 6: Performance comparison on 8-Puzzle in the multi-solution scenario.\nModel IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 0.00 2.52 2.52 6.72 6.72 36.97 52.10 0.00 1.43 1.54 2.52 3.36 21.15 27.45 GPT-3.5 #Sol 2.47 2.05 1.90 2.98 2.96 2.87 2.85 LLM invoked 1.00 1.00 10.00 55.86 24.18 1.00 4.19 fθ invoked - - - - - 36.25 52.06 Acc. MultiAcc 2.52 10.92 11.76 13.45 20.17 50.42 82.35 0.84 7.84 6.58 5.60 16.61 29.13 76.33 GPT-4 #Sol 2.97 1.21 2.08 2.97 2.70 2.97 1.52 LLM invoked 1.00 1.00 10.00 54.13 22.76 1.00 4.30 fθ invoked - - - - - 36.25 66.66",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 38 | with revision in the 8-Puzzle task, outperforming the best baseline, ToT (b=3), which only achieves 13.45% accuracy. Additionally, XOT demonstrates efficiency, requiring approximately 1.5 LLM calls and around 55 calls to fθ, while delivering significantly superior performance.
The multi-solution performance presented in Table 6 confirms that the XOT method continues to outperform other baselines for both GPT-3.5 and GPT-4 models in terms of accuracy and MultiAcc, whether or not revision is applied. Itâs worth noting that the revision process is particularly beneficial for GPT-4, as it improves the MultiAcc from 29.13% to 76.33%. These results once again demon- strate that XOT can effectively generate complex thought structures for complete multi-solutions with high performance and efficiency, making it particularly suitable for this task.
4.3 POCKET CUBE
The 2 Ã 2 Pocket Cube is a simplified variant of the classic Rubikâs Cube puzzle. Its primary objec- tive is to restore all of its faces to a uniform color by executing various face rotations. The maximum number of steps required to optimally solve the cube is 11, and it is also a NP-complete problem Demaine et al. (2017) and may possess multiple solutions. This task is known to be challenging to LLMs cub. | 2311.04254#38 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 38,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "with revision in the 8-Puzzle task, outperforming the best baseline, ToT (b=3), which only achieves 13.45% accuracy. Additionally, XOT demonstrates efficiency, requiring approximately 1.5 LLM calls and around 55 calls to fθ, while delivering significantly superior performance.\nThe multi-solution performance presented in Table 6 confirms that the XOT method continues to outperform other baselines for both GPT-3.5 and GPT-4 models in terms of accuracy and MultiAcc, whether or not revision is applied. Itâs worth noting that the revision process is particularly beneficial for GPT-4, as it improves the MultiAcc from 29.13% to 76.33%. These results once again demon- strate that XOT can effectively generate complex thought structures for complete multi-solutions with high performance and efficiency, making it particularly suitable for this task.\n4.3 POCKET CUBE\nThe 2 à 2 Pocket Cube is a simplified variant of the classic Rubikâs Cube puzzle. Its primary objec- tive is to restore all of its faces to a uniform color by executing various face rotations. The maximum number of steps required to optimally solve the cube is 11, and it is also a NP-complete problem Demaine et al. (2017) and may possess multiple solutions. This task is known to be challenging to LLMs cub.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 39 | 4.3.1 TASK SETUP
We initially set all faces of the cube to a uniform color and then randomly apply 5 actions sequen- tially selected from the 27 legal actions of the Rubikâs Cube. This process resulted in the creation of 1,000 training samples and 183 testing samples. All generated problems can be solved within 4 steps. To simplify the action space, we reduced the 27 legal operations to 9 actions, namely: {U, Uâ, U2, R, Râ, R2, F, Fâ, F2}, which are used in our experiments with both baselines and XOT. As shown in Table 1, the thoughts pertain to the step-by-step rotation, and the cube state after the move.
4.3.2 BASELINES & XOT SETUP
The IO prompt is augmented with a single in-context example. In CoT, we enrich each input-output pair by including intermediate actions and states. In ToT, we retrieve one-step thought candidates from the LLM at each stage and instruct the LLM to classify each candidate for intermediate selec- tion. A maximum step limit of 4 is imposed, as all generated problems can be resolved within this range. The cubeâs rules are conveyed through a system message, which includes the definition of the
10
Table 7: Performance comparison on Pocket Cube. | 2311.04254#39 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 39,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.3.1 TASK SETUP\nWe initially set all faces of the cube to a uniform color and then randomly apply 5 actions sequen- tially selected from the 27 legal actions of the Rubikâs Cube. This process resulted in the creation of 1,000 training samples and 183 testing samples. All generated problems can be solved within 4 steps. To simplify the action space, we reduced the 27 legal operations to 9 actions, namely: {U, Uâ, U2, R, Râ, R2, F, Fâ, F2}, which are used in our experiments with both baselines and XOT. As shown in Table 1, the thoughts pertain to the step-by-step rotation, and the cube state after the move.\n4.3.2 BASELINES & XOT SETUP\nThe IO prompt is augmented with a single in-context example. In CoT, we enrich each input-output pair by including intermediate actions and states. In ToT, we retrieve one-step thought candidates from the LLM at each stage and instruct the LLM to classify each candidate for intermediate selec- tion. A maximum step limit of 4 is imposed, as all generated problems can be resolved within this range. The cubeâs rules are conveyed through a system message, which includes the definition of the\n10\nTable 7: Performance comparison on Pocket Cube.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 40 | 10
Table 7: Performance comparison on Pocket Cube.
Model IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 1.09 0.00 0.00 7.65 17.49 1.64 45.36 74.32 1.00 1.00 10.00 16.50 58.72 8.93 1.00 1.55 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 18.69 64.63 1.09 1.09 1.09 11.48 19.57 18.03 45.90 77.60 1.00 1.00 10.00 16.39 56.58 8.55 1.00 1.54 fθ invoked - - - - - - 18.86 75.51
Table 8: Performance comparison on Pocket Cube in the multi-solution scenario. | 2311.04254#40 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 40,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "10\nTable 7: Performance comparison on Pocket Cube.\nModel IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 1.09 0.00 0.00 7.65 17.49 1.64 45.36 74.32 1.00 1.00 10.00 16.50 58.72 8.93 1.00 1.55 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 18.69 64.63 1.09 1.09 1.09 11.48 19.57 18.03 45.90 77.60 1.00 1.00 10.00 16.39 56.58 8.55 1.00 1.54 fθ invoked - - - - - - 18.86 75.51\nTable 8: Performance comparison on Pocket Cube in the multi-solution scenario.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 41 | Table 8: Performance comparison on Pocket Cube in the multi-solution scenario.
Model IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 0.55 0.55 0.55 17.49 3.28 39.89 73.22 0.27 0.55 0.18 5.83 1.09 23.04 48.72 GPT-3.5 #Sol 2.00 1.05 2.90 2.99 2.99 2.68 2.20 LLM invoked 1.00 1.00 10.00 58.72 14.76 1.00 4.13 fθ invoked Acc. MultiAcc - - - - - 18.95 115.73 2.19 1.64 1.63 19.57 30.50 47.54 91.26 1.09 0.82 0.82 6.52 16.85 31.97 77.41 GPT-4 #Sol 1.98 1.91 2.92 2.99 2.77 2.62 1.72 LLM invoked 1.00 1.00 1.00 56.58 13.36 1.00 4.08 fθ invoked - - - - - 18.95 122.54 | 2311.04254#41 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 41,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Table 8: Performance comparison on Pocket Cube in the multi-solution scenario.\nModel IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 0.55 0.55 0.55 17.49 3.28 39.89 73.22 0.27 0.55 0.18 5.83 1.09 23.04 48.72 GPT-3.5 #Sol 2.00 1.05 2.90 2.99 2.99 2.68 2.20 LLM invoked 1.00 1.00 10.00 58.72 14.76 1.00 4.13 fθ invoked Acc. MultiAcc - - - - - 18.95 115.73 2.19 1.64 1.63 19.57 30.50 47.54 91.26 1.09 0.82 0.82 6.52 16.85 31.97 77.41 GPT-4 #Sol 1.98 1.91 2.92 2.99 2.77 2.62 1.72 LLM invoked 1.00 1.00 1.00 56.58 13.36 1.00 4.08 fθ invoked - - - - - 18.95 122.54",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 42 | action space and illustrations of the execution of each action. For XOT, we conduct 20 simulations for each action taken and increase it to 500 for revision.
In the multi-solution setup, the IO, CoT, and CoT-SC prompts each include 3 examples, and each problem within these prompts offers 3 unique solutions. As for ToT (b=3) and GoT (k=3), the maximum number of steps allowed is extended to 7. In the case of XOT, after conducting MCTS simulations, we gather 50 thought trajectories, and we keep the top 3 thoughts with the highest counts.
4.3.3 RESULTS | 2311.04254#42 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 42,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "action space and illustrations of the execution of each action. For XOT, we conduct 20 simulations for each action taken and increase it to 500 for revision.\nIn the multi-solution setup, the IO, CoT, and CoT-SC prompts each include 3 examples, and each problem within these prompts offers 3 unique solutions. As for ToT (b=3) and GoT (k=3), the maximum number of steps allowed is extended to 7. In the case of XOT, after conducting MCTS simulations, we gather 50 thought trajectories, and we keep the top 3 thoughts with the highest counts.\n4.3.3 RESULTS",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 43 | 4.3.3 RESULTS
The Pocket Cube task, similar to the 8-Puzzle, poses a challenge that demands spatial imagination skills, making it difficult for LLMs to excel. As expected, most of the baselines show very poor performance in this task, with some baselines achieving 0% accuracy. The best-performing base- line, ToT (b=3) with GPT-4, only attains a success rate of 19.57%. In contrast, XOT can achieve over 45% accuracy without revision and over 75% accuracy with revision, establishing itself as an expert in solving this task. This success is attributed to the injection of external knowledge from MCTS, enabling LLMs to solve problems that they would struggle with on their own. Notably, XOT maintains high efficiency in this task, requiring only 1.55 and 1.54 LLM inference calls for GPT-3.5 and GPT-4, respectively. These results position XOT as a superior solution for enhancing LLMs in addressing seemingly insurmountable tasks. | 2311.04254#43 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 43,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.3.3 RESULTS\nThe Pocket Cube task, similar to the 8-Puzzle, poses a challenge that demands spatial imagination skills, making it difficult for LLMs to excel. As expected, most of the baselines show very poor performance in this task, with some baselines achieving 0% accuracy. The best-performing base- line, ToT (b=3) with GPT-4, only attains a success rate of 19.57%. In contrast, XOT can achieve over 45% accuracy without revision and over 75% accuracy with revision, establishing itself as an expert in solving this task. This success is attributed to the injection of external knowledge from MCTS, enabling LLMs to solve problems that they would struggle with on their own. Notably, XOT maintains high efficiency in this task, requiring only 1.55 and 1.54 LLM inference calls for GPT-3.5 and GPT-4, respectively. These results position XOT as a superior solution for enhancing LLMs in addressing seemingly insurmountable tasks.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 44 | In the case of the multi-solution scenario, the performance of the XOT method remains remarkable, achieving over 91% accuracy and over 77% MultiAcc with GPT-4. The revision process continues to play an important role, significantly improving the performance of XOT with both GPT models. The closest competitor in this setting is GoT (k=3) with GPT-4, which achieves an accuracy of 30.50% and a MultiAcc of 16.85%, but it requires a significantly higher number of LLM invocations compared to XOT (13.36 vs. 4.08). Overall, XOT retains its position as the best solution for the Pocket Cube task, exhibiting high performance, efficiency, and flexibility.
4.4 ABLATION STUDY
In our ablation study, we consider two aspects: the impact of the number of revisions on the perfor- mance and efficiency of XOT and the sensitivity of performance to the completeness of the provided thoughts. These angles allow us to gain insights into how XOTâs performance can be improved and understand the importance of providing complete thoughts in complex problem-solving tasks.
11
(a) Game of 24 (b) 8-Puzzle
# (c) Pocket Cube
Figure 4: Accuracy, LLM and fθ invoked comparison on XOT w.r.t. the number of revisions.
4.4.1 NUMBER OF REVISIONS | 2311.04254#44 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 44,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "In the case of the multi-solution scenario, the performance of the XOT method remains remarkable, achieving over 91% accuracy and over 77% MultiAcc with GPT-4. The revision process continues to play an important role, significantly improving the performance of XOT with both GPT models. The closest competitor in this setting is GoT (k=3) with GPT-4, which achieves an accuracy of 30.50% and a MultiAcc of 16.85%, but it requires a significantly higher number of LLM invocations compared to XOT (13.36 vs. 4.08). Overall, XOT retains its position as the best solution for the Pocket Cube task, exhibiting high performance, efficiency, and flexibility.\n4.4 ABLATION STUDY\nIn our ablation study, we consider two aspects: the impact of the number of revisions on the perfor- mance and efficiency of XOT and the sensitivity of performance to the completeness of the provided thoughts. These angles allow us to gain insights into how XOTâs performance can be improved and understand the importance of providing complete thoughts in complex problem-solving tasks.\n11\n(a) Game of 24 (b) 8-Puzzle \n# (c) Pocket Cube\nFigure 4: Accuracy, LLM and fθ invoked comparison on XOT w.r.t. the number of revisions.\n4.4.1 NUMBER OF REVISIONS",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 45 | Figure 4: Accuracy, LLM and fθ invoked comparison on XOT w.r.t. the number of revisions.
4.4.1 NUMBER OF REVISIONS
Itâs important to highlight that the performance of each task can be further improved through multi- ple revisions of the thought using the MCTS-LLM collaborative framework. In Fig. 4, we compare the performance of GPT-3.5 and GPT-4 models using the XOT method with varying numbers of revisions, ranging from 0 to 3, across all three tasks. | 2311.04254#45 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 45,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Figure 4: Accuracy, LLM and fθ invoked comparison on XOT w.r.t. the number of revisions.\n4.4.1 NUMBER OF REVISIONS\nItâs important to highlight that the performance of each task can be further improved through multi- ple revisions of the thought using the MCTS-LLM collaborative framework. In Fig. 4, we compare the performance of GPT-3.5 and GPT-4 models using the XOT method with varying numbers of revisions, ranging from 0 to 3, across all three tasks.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 46 | In the Game of 24 task, as the number of revisions increases, both models exhibit improved per- formance. Notably, GPT-3.5 consistently outperforms GPT-4 in terms of accuracy. After three revisions, GPT-3.5 achieves an accuracy of 90.51%, while GPT-4 reaches 85.40%. This improved performance comes at the cost of increased inference times and model calls, primarily driven by the need for more interactions to generate revised thoughts. For the 8-Puzzle task, the trend of in- creasing accuracy with more revisions remains valid. However, in this task, GPT-4 significantly outperforms GPT-3.5. After one revision, GPT-4 achieves an accuracy of 93.28%, which increases to 95.8% after the third revision. In contrast, GPT-3.5 only attains an accuracy of 63.03% after the third revision. In the Pocket Cube task, the performance trend is similar. The accuracy of both mod- els improves with an increase in the number of revisions. GPT-3.5 starts at an accuracy of 45.36% without revision and improves to 84.70% after three revisions. GPT-4 | 2311.04254#46 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 46,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "In the Game of 24 task, as the number of revisions increases, both models exhibit improved per- formance. Notably, GPT-3.5 consistently outperforms GPT-4 in terms of accuracy. After three revisions, GPT-3.5 achieves an accuracy of 90.51%, while GPT-4 reaches 85.40%. This improved performance comes at the cost of increased inference times and model calls, primarily driven by the need for more interactions to generate revised thoughts. For the 8-Puzzle task, the trend of in- creasing accuracy with more revisions remains valid. However, in this task, GPT-4 significantly outperforms GPT-3.5. After one revision, GPT-4 achieves an accuracy of 93.28%, which increases to 95.8% after the third revision. In contrast, GPT-3.5 only attains an accuracy of 63.03% after the third revision. In the Pocket Cube task, the performance trend is similar. The accuracy of both mod- els improves with an increase in the number of revisions. GPT-3.5 starts at an accuracy of 45.36% without revision and improves to 84.70% after three revisions. GPT-4",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 48 | Note that the number of LLM invocations does not increase dramatically with additional revisions, even though fθ is called more times to guide simulations. Considering the significant disparity in in- ference costs between LLM and fθ, increasing the number of revisions to achieve better performance appears to be a favorable trade-off.
12
# Table 9: Performance comparison on three tasks with incomplete thoughts. | 2311.04254#48 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 48,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Note that the number of LLM invocations does not increase dramatically with additional revisions, even though fθ is called more times to guide simulations. Considering the significant disparity in in- ference costs between LLM and fθ, increasing the number of revisions to achieve better performance appears to be a favorable trade-off.\n12\n# Table 9: Performance comparison on three tasks with incomplete thoughts.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 49 | 12
# Table 9: Performance comparison on three tasks with incomplete thoughts.
Task Game of 24 8-Puzzle Pocket Cube Model ToT (b=1) GoT (k=1) XoT (w/o revise) ToT (b=1) GoT (k=1) XoT (w/o revise) ToT (b=1) GoT (k=1) XoT (w/o revise) GPT-3.5 Acc. [%] LLM invoked 3.65 2.19 17.52 0.00 0.00 2.52 0.55 0.00 5.46 17.15 5.00 1.00 32.60 18.63 1.00 16.48 8.96 1.00 GPT-4 fθ invoked Acc. [%] LLM invoked - - 68.73 - - 36.66 - - 18.85 40.88 9.49 43.07 6.72 3.36 40.34 2.19 1.64 6.01 18.55 5.00 1.00 26.98 19.00 1.00 16.39 8.68 1.00 fθ invoked - - 68.70 - - 36.24 - - 18.89 | 2311.04254#49 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 49,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "12\n# Table 9: Performance comparison on three tasks with incomplete thoughts.\nTask Game of 24 8-Puzzle Pocket Cube Model ToT (b=1) GoT (k=1) XoT (w/o revise) ToT (b=1) GoT (k=1) XoT (w/o revise) ToT (b=1) GoT (k=1) XoT (w/o revise) GPT-3.5 Acc. [%] LLM invoked 3.65 2.19 17.52 0.00 0.00 2.52 0.55 0.00 5.46 17.15 5.00 1.00 32.60 18.63 1.00 16.48 8.96 1.00 GPT-4 fθ invoked Acc. [%] LLM invoked - - 68.73 - - 36.66 - - 18.85 40.88 9.49 43.07 6.72 3.36 40.34 2.19 1.64 6.01 18.55 5.00 1.00 26.98 19.00 1.00 16.39 8.68 1.00 fθ invoked - - 68.70 - - 36.24 - - 18.89",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 50 | Game of 24 8-Puzzle Pocket Cube Initial State Initial State Initial State igh Left les a) ⬠S J5 x G+@x7)41 PB ax3)+8x7) CIS) ee EI yy 4) [y XV Left G+GxIx1 Nera | yw 67/8) Final State Final State Final State
Figure 5: Examples of thought structures generated by XOT for all three tasks in the multi-solution scenario.
4.4.2 INCOMPLETE THOUGHT
In this ablation study, we explore the performance of LLMs when provided with incomplete thoughts, specifically omitting the last step of the thought trajectory. This simulates scenarios where MCTS might supply inaccurate or incomplete thoughts. The aim is to test whether LLMs can inde- pendently solve problems or rely on their own reasoning, rather than solely relying on the thought from MCTS as answers. We present the performance comparison for all three tasks in Table 9. Note that we only compare ToT and GoT since other baselines do not support this comparison by their nature. | 2311.04254#50 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 50,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Game of 24 8-Puzzle Pocket Cube Initial State Initial State Initial State igh Left les a) ⬠S J5 x G+@x7)41 PB ax3)+8x7) CIS) ee EI yy 4) [y XV Left G+GxIx1 Nera | yw 67/8) Final State Final State Final State\nFigure 5: Examples of thought structures generated by XOT for all three tasks in the multi-solution scenario.\n4.4.2 INCOMPLETE THOUGHT\nIn this ablation study, we explore the performance of LLMs when provided with incomplete thoughts, specifically omitting the last step of the thought trajectory. This simulates scenarios where MCTS might supply inaccurate or incomplete thoughts. The aim is to test whether LLMs can inde- pendently solve problems or rely on their own reasoning, rather than solely relying on the thought from MCTS as answers. We present the performance comparison for all three tasks in Table 9. Note that we only compare ToT and GoT since other baselines do not support this comparison by their nature.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 51 | The results clearly show that incomplete thoughts lead to a significant performance drop in all three tasks. GPT-3.5 is more affected than GPT-4, with GPT-3.5 achieving 0% accuracy on several base- lines. In contrast, XOT with GPT-4 attains satisfactory performance on the Game of 24 and 8- Puzzle, achieving over 40% accuracy. However, the performance of XOT is dramatically affected in the Pocket Cube task, with accuracy dropping to 6%. This demonstrates that for very complex tasks, LLMs are highly sensitive to the completeness of the thoughts provided. Missing steps in the thought can lead to a substantial drop in performance, highlighting the importance of providing complete thoughts for such tasks.
4.5 CASE STUDY
Finally, in Fig. 5, we provide examples of thought structures generated by XOT for all three tasks in the multi-solution scenario. It is noteworthy that, owing to the multiple solutions required, the generated thoughts intertwine during intermediate steps and converge towards the final goal state. This results in a naturally woven thought structure resembling a graph, showcasing the remarkable flexibility achieved by XOT. Upon closer examination of each example, in the case of the Game of 24, there are multiple solutions to reach the goal of 24 from the initial state. XOT effectively
13 | 2311.04254#51 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 51,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "The results clearly show that incomplete thoughts lead to a significant performance drop in all three tasks. GPT-3.5 is more affected than GPT-4, with GPT-3.5 achieving 0% accuracy on several base- lines. In contrast, XOT with GPT-4 attains satisfactory performance on the Game of 24 and 8- Puzzle, achieving over 40% accuracy. However, the performance of XOT is dramatically affected in the Pocket Cube task, with accuracy dropping to 6%. This demonstrates that for very complex tasks, LLMs are highly sensitive to the completeness of the thoughts provided. Missing steps in the thought can lead to a substantial drop in performance, highlighting the importance of providing complete thoughts for such tasks.\n4.5 CASE STUDY\nFinally, in Fig. 5, we provide examples of thought structures generated by XOT for all three tasks in the multi-solution scenario. It is noteworthy that, owing to the multiple solutions required, the generated thoughts intertwine during intermediate steps and converge towards the final goal state. This results in a naturally woven thought structure resembling a graph, showcasing the remarkable flexibility achieved by XOT. Upon closer examination of each example, in the case of the Game of 24, there are multiple solutions to reach the goal of 24 from the initial state. XOT effectively\n13",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 52 | 13
predicts these trajectories, indicating its ability to grasp complex thought structures. In the 8-Puzzle example, we observe instances of reflection in the thought structure, with back-and-forth recurrent state transitions. This demonstrates XOTâs capacity for self-reflection, a crucial attribute for LLMs, as discussed in previous work Shinn et al. (2023). In the case of the Pocket Cube, XOT identifies four distinct pathways to reach the goal state, leading to successful problem-solving across multiple solutions.
Overall, these cases highlight how XOT encapsulates the flexibility required in thought generation, fostering diverse and creative thinking for LLMs. This enables them to produce multiple high- quality answers to a single problem effectively.
4.6 EXPERIMENT SUMMARY
In summary, our approach XOT significantly improves the performance of LLMs by introducing a streamlined thought trajectory revision process. This represents a fundamental shift from traditional problem-solving approaches, resulting in substantial performance enhancements across a range of tasks. Notably, XOT excels in solving the Game of 24 and demonstrates its ability to overcome challenges requiring spatial reasoning, such as the 8-Puzzle and Pocket Cube, which were previously challenging for LLMs. The remarkable synergy of improved performance, efficiency, and flexibility exhibited by XOT positions it as an exemplary and superior method for eliciting optimal responses from LLMs.
5 RELATED WORK | 2311.04254#52 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 52,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "13\npredicts these trajectories, indicating its ability to grasp complex thought structures. In the 8-Puzzle example, we observe instances of reflection in the thought structure, with back-and-forth recurrent state transitions. This demonstrates XOTâs capacity for self-reflection, a crucial attribute for LLMs, as discussed in previous work Shinn et al. (2023). In the case of the Pocket Cube, XOT identifies four distinct pathways to reach the goal state, leading to successful problem-solving across multiple solutions.\nOverall, these cases highlight how XOT encapsulates the flexibility required in thought generation, fostering diverse and creative thinking for LLMs. This enables them to produce multiple high- quality answers to a single problem effectively.\n4.6 EXPERIMENT SUMMARY\nIn summary, our approach XOT significantly improves the performance of LLMs by introducing a streamlined thought trajectory revision process. This represents a fundamental shift from traditional problem-solving approaches, resulting in substantial performance enhancements across a range of tasks. Notably, XOT excels in solving the Game of 24 and demonstrates its ability to overcome challenges requiring spatial reasoning, such as the 8-Puzzle and Pocket Cube, which were previously challenging for LLMs. The remarkable synergy of improved performance, efficiency, and flexibility exhibited by XOT positions it as an exemplary and superior method for eliciting optimal responses from LLMs.\n5 RELATED WORK",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 53 | 5 RELATED WORK
Decision Making & Planning with LLMs. The utilization of LLMs for decision-making and plan- ning has become a prominent area of research. Similar to human problem-solving, the process in- volves breaking down complex problems into sub-tasks. Various frameworks, such as CoT Wei et al. (2022), ToT Yao et al. (2023), and GoT Besta et al. (2023), have been designed to facilitate prob- lem decomposition in different structural forms, leading to enhanced solutions derived from LLMs. Extensions of these frameworks have also been explored across different domains and modalities Zhang et al. (2022; 2023); Ning et al. (2023); Turpin et al. (2023); Long (2023). Our approach XOT distinguishes itself from the aforementioned work by concurrently achieving superior performance, efficiency, and flexibility, embodying the concept of comprehensive thought generation. | 2311.04254#53 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 53,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "5 RELATED WORK\nDecision Making & Planning with LLMs. The utilization of LLMs for decision-making and plan- ning has become a prominent area of research. Similar to human problem-solving, the process in- volves breaking down complex problems into sub-tasks. Various frameworks, such as CoT Wei et al. (2022), ToT Yao et al. (2023), and GoT Besta et al. (2023), have been designed to facilitate prob- lem decomposition in different structural forms, leading to enhanced solutions derived from LLMs. Extensions of these frameworks have also been explored across different domains and modalities Zhang et al. (2022; 2023); Ning et al. (2023); Turpin et al. (2023); Long (2023). Our approach XOT distinguishes itself from the aforementioned work by concurrently achieving superior performance, efficiency, and flexibility, embodying the concept of comprehensive thought generation.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 54 | Furthermore, the âDescribe, Explain, Plan, and Selectâ framework introduced in Wang et al. (2023b) presents an interactive planning approach for LLMs, significantly enhancing planning performance for multi-task agents. Research conducted in Singh et al. (2023) leverages LLMs to suggest next actions or sequences during task planning for robotics, leading to improved task performance across various metrics. Additionally, work presented in Xie et al. (2023) employs LLMs to translate natural language into planning goals, demonstrating their capacity to harness commonsense knowledge and reasoning to provide missing details for under-specified goals. These studies underscore the growing potential of LLMs in the field of planning, with research efforts expanding rapidly. | 2311.04254#54 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 54,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Furthermore, the âDescribe, Explain, Plan, and Selectâ framework introduced in Wang et al. (2023b) presents an interactive planning approach for LLMs, significantly enhancing planning performance for multi-task agents. Research conducted in Singh et al. (2023) leverages LLMs to suggest next actions or sequences during task planning for robotics, leading to improved task performance across various metrics. Additionally, work presented in Xie et al. (2023) employs LLMs to translate natural language into planning goals, demonstrating their capacity to harness commonsense knowledge and reasoning to provide missing details for under-specified goals. These studies underscore the growing potential of LLMs in the field of planning, with research efforts expanding rapidly.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 55 | Augmenting LLMs with RL. Enhancing the capabilities of LLMs through the incorporation of ex- ternal models constitutes an effective strategy for improving their overall quality. The foundational work of ChatGPT Ouyang et al. (2022) leverages RL from human feedback to enable LLMs to ad- here to human guidance, resulting in a substantial enhancement of their truthfulness and a reduction in toxic output. Similarly, GLAM Carta et al. (2023) employs online RL to establish alignment between LLMsâ knowledge and the broader environment, thus enhancing their ability to generalize to new objects or tasks and ultimately improving their performance. Additionally, an interesting study in Yuan et al. (2023) utilizes RL to acquire basic skills in the context of Minecraft Cipollone et al. (2014), with subsequent high-level planning carried out by LLMs. This approach demon- strates promising performance across various Minecraft tasks. Furthermore, the ESPER framework Yu et al. (2023) harnesses RL to achieve alignment between multimodal inputs and language model generations, all without the need for direct supervision. This empowers LLMs to effectively tackle multimodal tasks and provides robust visual alignment and rapid inference speeds while preserving the textual domain. Collectively, these research endeavors underscore the considerable potential in augmenting LLMs with reinforcement learning techniques.
14
# 6 DISCUSSION | 2311.04254#55 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 55,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Augmenting LLMs with RL. Enhancing the capabilities of LLMs through the incorporation of ex- ternal models constitutes an effective strategy for improving their overall quality. The foundational work of ChatGPT Ouyang et al. (2022) leverages RL from human feedback to enable LLMs to ad- here to human guidance, resulting in a substantial enhancement of their truthfulness and a reduction in toxic output. Similarly, GLAM Carta et al. (2023) employs online RL to establish alignment between LLMsâ knowledge and the broader environment, thus enhancing their ability to generalize to new objects or tasks and ultimately improving their performance. Additionally, an interesting study in Yuan et al. (2023) utilizes RL to acquire basic skills in the context of Minecraft Cipollone et al. (2014), with subsequent high-level planning carried out by LLMs. This approach demon- strates promising performance across various Minecraft tasks. Furthermore, the ESPER framework Yu et al. (2023) harnesses RL to achieve alignment between multimodal inputs and language model generations, all without the need for direct supervision. This empowers LLMs to effectively tackle multimodal tasks and provides robust visual alignment and rapid inference speeds while preserving the textual domain. Collectively, these research endeavors underscore the considerable potential in augmenting LLMs with reinforcement learning techniques.\n14\n# 6 DISCUSSION",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 56 | 14
# 6 DISCUSSION
Generalization While XOT is presently utilized for reasoning and search problems, its applicability can be extended to a broader spectrum of problem domains characterized by decomposable tasks with well-defined objectives. The MCTS utilized in XOT is particularly suitable for such tasks and can therefore generalize to more complex problems. We also note that MCTS is functioning in a supportive role and can be substituted with alternative supervised or RL models for thought exploration and generation, which can serve as a copilot to inject domain knowledge of the real- world model to LLMs. This opens up a promising avenue for future research, enabling LLMs to engage in more effective planning and problem solving processes. | 2311.04254#56 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 56,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "14\n# 6 DISCUSSION\nGeneralization While XOT is presently utilized for reasoning and search problems, its applicability can be extended to a broader spectrum of problem domains characterized by decomposable tasks with well-defined objectives. The MCTS utilized in XOT is particularly suitable for such tasks and can therefore generalize to more complex problems. We also note that MCTS is functioning in a supportive role and can be substituted with alternative supervised or RL models for thought exploration and generation, which can serve as a copilot to inject domain knowledge of the real- world model to LLMs. This opens up a promising avenue for future research, enabling LLMs to engage in more effective planning and problem solving processes.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 57 | Limitation We also note that the implementation of XOT necessitates the training of additional pol- icy and value models to expedite the inference process. This training process requires the acquisition of datasets from real-world environments, introducing supplementary costs and efforts. However, note that these policy and value models are considerably smaller and more computationally efficient than the underlying LLMs. Consequently, the incurred costs are deemed low, particularly in the con- text of tasks featured in this study, where the thought steps and objectives are well-defined. In future research endeavors, we intend to explore methods to enhance the efficiency of the training process for XOT in scenarios where the objectives are less straightforward, such as multi-agent planning and code generation tasks Talebirad & Nadiri (2023); Vaithilingam et al. (2022). This endeavor will expand the applicability of the proposed XOT framework to a broader range of applications. | 2311.04254#57 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 57,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Limitation We also note that the implementation of XOT necessitates the training of additional pol- icy and value models to expedite the inference process. This training process requires the acquisition of datasets from real-world environments, introducing supplementary costs and efforts. However, note that these policy and value models are considerably smaller and more computationally efficient than the underlying LLMs. Consequently, the incurred costs are deemed low, particularly in the con- text of tasks featured in this study, where the thought steps and objectives are well-defined. In future research endeavors, we intend to explore methods to enhance the efficiency of the training process for XOT in scenarios where the objectives are less straightforward, such as multi-agent planning and code generation tasks Talebirad & Nadiri (2023); Vaithilingam et al. (2022). This endeavor will expand the applicability of the proposed XOT framework to a broader range of applications.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 58 | Conclusion The XOT framework presented in this paper signifies a significant progression in It challenges the constraints of thought generation for LLMs aimed at solving complex tasks. the âPenrose Triangle â by concurrently achieving performance, efficiency, and flexibility, a feat unattainable by existing prompting paradigms. This accomplishment is achieved through the inte- gration of MCTS with pretrained low-cost policy and value networks, by injecting domain knowl- edge into LLMs, offloading thought searching, and facilitating unconstrained free-style thought ex- ploration. The collaborative thought revision framework involving MCTS and LLM further en- hances the quality of thought generation. Experimental evaluations conducted across three intricate real-world problems, namely the Game of 24, 8-Puzzle, and Pocket Cube, provide empirical evi- dence that our XOT framework significantly outperforms existing prompting paradigms, particularly in scenarios involving multi-solution problems. | 2311.04254#58 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 58,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Conclusion The XOT framework presented in this paper signifies a significant progression in It challenges the constraints of thought generation for LLMs aimed at solving complex tasks. the âPenrose Triangle â by concurrently achieving performance, efficiency, and flexibility, a feat unattainable by existing prompting paradigms. This accomplishment is achieved through the inte- gration of MCTS with pretrained low-cost policy and value networks, by injecting domain knowl- edge into LLMs, offloading thought searching, and facilitating unconstrained free-style thought ex- ploration. The collaborative thought revision framework involving MCTS and LLM further en- hances the quality of thought generation. Experimental evaluations conducted across three intricate real-world problems, namely the Game of 24, 8-Puzzle, and Pocket Cube, provide empirical evi- dence that our XOT framework significantly outperforms existing prompting paradigms, particularly in scenarios involving multi-solution problems.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 59 | # REFERENCES
4 Numbers. https://www.4nums.com/game/difficulties/. [Online; accessed 21-Sep- 2023].
I Calculated ChatGPTâs IQ. https://www.youtube.com/watch?v=HXb9Azzhr1k. Ac- cessed: 2023-10-30.
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687, 2023.
Thomas Carta, Cl´ement Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. Grounding large language models in interactive environments with online reinforcement learning. arXiv preprint arXiv:2302.02662, 2023. | 2311.04254#59 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 59,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "# REFERENCES\n4 Numbers. https://www.4nums.com/game/difficulties/. [Online; accessed 21-Sep- 2023].\nI Calculated ChatGPTâs IQ. https://www.youtube.com/watch?v=HXb9Azzhr1k. Ac- cessed: 2023-10-30.\nMaciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687, 2023.\nThomas Carta, Cl´ement Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. Grounding large language models in interactive environments with online reinforcement learning. arXiv preprint arXiv:2302.02662, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 60 | Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang, Xin Gao, Liu Shi, Yunjie Cao, Xuedong Gao, Hao Fan, Ming Wen, et al. Empowering practical root cause analysis by large language models for cloud incidents. arXiv preprint arXiv:2305.15778, 2023.
Maria Cipollone, Catherine C Schifter, and Rick A Moffat. Minecraft as a creative tool: A case study. International Journal of Game-Based Learning (IJGBL), 4(2):1â14, 2014.
Erik D Demaine, Sarah Eisenstat, and Mikhail Rudoy. Solving the rubikâs cube optimally is np- complete. arXiv preprint arXiv:1706.06708, 2017.
15
Haakon Faste and Honray Lin. The untapped promise of digital mind maps. In Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1017â1026, 2012.
Simon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, and Julius Berner. Mathematical capabilities of chatgpt. arXiv preprint arXiv:2301.13867, 2023. | 2311.04254#60 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 60,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang, Xin Gao, Liu Shi, Yunjie Cao, Xuedong Gao, Hao Fan, Ming Wen, et al. Empowering practical root cause analysis by large language models for cloud incidents. arXiv preprint arXiv:2305.15778, 2023.\nMaria Cipollone, Catherine C Schifter, and Rick A Moffat. Minecraft as a creative tool: A case study. International Journal of Game-Based Learning (IJGBL), 4(2):1â14, 2014.\nErik D Demaine, Sarah Eisenstat, and Mikhail Rudoy. Solving the rubikâs cube optimally is np- complete. arXiv preprint arXiv:1706.06708, 2017.\n15\nHaakon Faste and Honray Lin. The untapped promise of digital mind maps. In Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1017â1026, 2012.\nSimon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, and Julius Berner. Mathematical capabilities of chatgpt. arXiv preprint arXiv:2301.13867, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 61 | Aur´elien Garivier and Eric Moulines. On upper-confidence bound policies for switching bandit problems. In International Conference on Algorithmic Learning Theory, pp. 174â188. Springer, 2011.
Peter Jamieson. Using modern graph analysis techniques on mind maps to help quantify learning. In 2012 Frontiers in Education Conference Proceedings, pp. 1â6. IEEE, 2012.
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. Causal reasoning and large language models: Opening a new frontier for causality. arXiv preprint arXiv:2305.00050, 2023.
Yuxi Li. Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274, 2017.
Jieyi Long. Large language model guided tree-of-thought. arXiv preprint arXiv:2305.08291, 2023.
Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, and Yu Wang. Skeleton-of-thought: Large language models can do parallel decoding. arXiv preprint arXiv:2307.15337, 2023. | 2311.04254#61 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 61,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Aur´elien Garivier and Eric Moulines. On upper-confidence bound policies for switching bandit problems. In International Conference on Algorithmic Learning Theory, pp. 174â188. Springer, 2011.\nPeter Jamieson. Using modern graph analysis techniques on mind maps to help quantify learning. In 2012 Frontiers in Education Conference Proceedings, pp. 1â6. IEEE, 2012.\nEmre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. Causal reasoning and large language models: Opening a new frontier for causality. arXiv preprint arXiv:2305.00050, 2023.\nYuxi Li. Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274, 2017.\nJieyi Long. Large language model guided tree-of-thought. arXiv preprint arXiv:2305.08291, 2023.\nXuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, and Yu Wang. Skeleton-of-thought: Large language models can do parallel decoding. arXiv preprint arXiv:2307.15337, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 62 | Reham Omar, Omij Mangukiya, Panos Kalnis, and Essam Mansour. Chatgpt versus traditional question answering for knowledge graphs: Current status and future directions towards knowl- edge graph chatbots. arXiv preprint arXiv:2302.06466, 2023.
OpenAI. Gpt-4 technical report, 2023.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730â27744, 2022.
Martin L Puterman. Markov decision processes. Handbooks in operations research and management science, 2:331â434, 1990.
Daniel Ratner and Manfred Warmuth. Finding a shortest solution for the n x n extension of the In Proceedings of the Fifth AAAI National Conference on Artificial 15-puzzle is intractable. Intelligence, pp. 168â172, 1986.
Christopher D Rosin. Multi-armed bandits with episode context. Annals of Mathematics and Artifi- cial Intelligence, 61(3):203â230, 2011. | 2311.04254#62 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 62,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Reham Omar, Omij Mangukiya, Panos Kalnis, and Essam Mansour. Chatgpt versus traditional question answering for knowledge graphs: Current status and future directions towards knowl- edge graph chatbots. arXiv preprint arXiv:2302.06466, 2023.\nOpenAI. Gpt-4 technical report, 2023.\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730â27744, 2022.\nMartin L Puterman. Markov decision processes. Handbooks in operations research and management science, 2:331â434, 1990.\nDaniel Ratner and Manfred Warmuth. Finding a shortest solution for the n x n extension of the In Proceedings of the Fifth AAAI National Conference on Artificial 15-puzzle is intractable. Intelligence, pp. 168â172, 1986.\nChristopher D Rosin. Multi-armed bandits with episode context. Annals of Mathematics and Artifi- cial Intelligence, 61(3):203â230, 2011.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 63 | Christopher D Rosin. Multi-armed bandits with episode context. Annals of Mathematics and Artifi- cial Intelligence, 61(3):203â230, 2011.
Noah Shinn, Beck Labash, and Ashwin Gopinath. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366, 2023.
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354â359, 2017.
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Progprompt: Generating situated robot task plans using In 2023 IEEE International Conference on Robotics and Automation large language models. (ICRA), pp. 11523â11530. IEEE, 2023. | 2311.04254#63 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 63,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Christopher D Rosin. Multi-armed bandits with episode context. Annals of Mathematics and Artifi- cial Intelligence, 61(3):203â230, 2011.\nNoah Shinn, Beck Labash, and Ashwin Gopinath. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366, 2023.\nDavid Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354â359, 2017.\nIshika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Progprompt: Generating situated robot task plans using In 2023 IEEE International Conference on Robotics and Automation large language models. (ICRA), pp. 11523â11530. IEEE, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 64 | Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati. Gpt-4 doesnât know itâs wrong: An analysis of iterative prompting for reasoning problems. arXiv preprint arXiv:2310.12397, 2023.
Yashar Talebirad and Amirhossein Nadiri. Multi-agent collaboration: Harnessing the power of intelligent llm agents. arXiv preprint arXiv:2306.03314, 2023.
16
Miles Turpin, Julian Michael, Ethan Perez, and Samuel R Bowman. Language models donât al- ways say what they think: Unfaithful explanations in chain-of-thought prompting. arXiv preprint arXiv:2305.04388, 2023.
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts, pp. 1â7, 2022.
Karthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati. Can large language models really improve by self-critiquing their own plans? arXiv preprint arXiv:2310.08118, 2023. | 2311.04254#64 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 64,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati. Gpt-4 doesnât know itâs wrong: An analysis of iterative prompting for reasoning problems. arXiv preprint arXiv:2310.12397, 2023.\nYashar Talebirad and Amirhossein Nadiri. Multi-agent collaboration: Harnessing the power of intelligent llm agents. arXiv preprint arXiv:2306.03314, 2023.\n16\nMiles Turpin, Julian Michael, Ethan Perez, and Samuel R Bowman. Language models donât al- ways say what they think: Unfaithful explanations in chain-of-thought prompting. arXiv preprint arXiv:2305.04388, 2023.\nPriyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts, pp. 1â7, 2022.\nKarthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati. Can large language models really improve by self-critiquing their own plans? arXiv preprint arXiv:2310.08118, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 65 | Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023a.
Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. arXiv preprint arXiv:2302.01560, 2023b.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824â24837, 2022.
Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. Translating natural lan- guage to planning goals with large-language models. arXiv preprint arXiv:2302.05128, 2023. | 2311.04254#65 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 65,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023a.\nZihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. arXiv preprint arXiv:2302.01560, 2023b.\nJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824â24837, 2022.\nYaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. Translating natural lan- guage to planning goals with large-language models. arXiv preprint arXiv:2302.05128, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.04254 | 66 | Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023.
Youngjae Yu, Jiwan Chung, Heeseung Yun, Jack Hessel, Jae Sung Park, Ximing Lu, Rowan Zellers, Prithviraj Ammanabrolu, Ronan Le Bras, Gunhee Kim, et al. Fusing pre-trained language mod- els with multimodal prompts through reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10845â10856, 2023.
Haoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie, Penglin Cai, Hao Dong, and Zongqing Lu. Plan4mc: Skill reinforcement learning and planning for open-world minecraft tasks. arXiv preprint arXiv:2303.16563, 2023.
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493, 2022. | 2311.04254#66 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 66,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023.\nYoungjae Yu, Jiwan Chung, Heeseung Yun, Jack Hessel, Jae Sung Park, Ximing Lu, Rowan Zellers, Prithviraj Ammanabrolu, Ronan Le Bras, Gunhee Kim, et al. Fusing pre-trained language mod- els with multimodal prompts through reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10845â10856, 2023.\nHaoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie, Penglin Cai, Hao Dong, and Zongqing Lu. Plan4mc: Skill reinforcement learning and planning for open-world minecraft tasks. arXiv preprint arXiv:2303.16563, 2023.\nZhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493, 2022.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} |
2311.01343 | 0 | 3 2 0 2 v o N 8 ] R I . s c [
3 v 3 4 3 1 0 . 1 1 3 2 : v i X r a
Collaborative Large Language Model for Recommender Systems Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1 1University of Virginia, 2LinkedIn Inc. 1{uqp4qh, jundong}@virginia.edu, 2{liawu, qguo, liahong}@linkedin.com
Liangjie Hongâ, Jundong Li! âLinkedIn Inc. qguo, liahong}@linkedin.com Recommendations QO Yes! tT retrieve few tural Language e.g., user t transform user interactions and features item 2 is a computer. (continuous or categorical) will user_1 buy a mouse? Thouse is a component of PC maybe she needs a mouse encoded knowledge reasoning ability has bought item 2. [e) oho is a CS student.
# ABSTRACT | 2311.01343#0 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 0,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3 2 0 2 v o N 8 ] R I . s c [\n3 v 3 4 3 1 0 . 1 1 3 2 : v i X r a\nCollaborative Large Language Model for Recommender Systems Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1 1University of Virginia, 2LinkedIn Inc. 1{uqp4qh, jundong}@virginia.edu, 2{liawu, qguo, liahong}@linkedin.com\nLiangjie Hongâ, Jundong Li! âLinkedIn Inc. qguo, liahong}@linkedin.com Recommendations QO Yes! tT retrieve few tural Language e.g., user t transform user interactions and features item 2 is a computer. (continuous or categorical) will user_1 buy a mouse? Thouse is a component of PC maybe she needs a mouse encoded knowledge reasoning ability has bought item 2. [e) oho is a CS student.\n# ABSTRACT",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 0 | # Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models
Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, & Sowon Hahn Human Factors Psychology Lab Seoul National University yoonlee78@snu.ac.kr, swhahn@snu.ac.kr
Standard Prompting Input: | just broke up. My life is over. Output: | am sorry to hear that you are sad Chain-of-Empathy Prompting (CoE) Input: | just broke up. My life is over, 1st Step : The client is feeling sad. Reasoning : (Emotion) (CoE) 2nd Step : The client shows (Cognitive catastrophic cognitive error. Error) Output :'m sorry to hear that. | understand you're feeling extremely upset due to your breakup. It's natural to) feel this way. Breakups are hard but they don't define) your whole existence or future. Let's work through! these emotions and explore ways to cope, Psychotherapy-based CoEs Focus "Cognitive Error" "Emotion Control" }-â2"Self-Understanding" }-â2"Decision Making"
Figure 1: Chain-of-Empathy (CoE) prompting with cognitive reasoning of humanâs emotion based on psychotherapy models.
# Abstract | 2311.04915#0 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 0,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models\nYoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, & Sowon Hahn Human Factors Psychology Lab Seoul National University yoonlee78@snu.ac.kr, swhahn@snu.ac.kr\nStandard Prompting Input: | just broke up. My life is over. Output: | am sorry to hear that you are sad Chain-of-Empathy Prompting (CoE) Input: | just broke up. My life is over, 1st Step : The client is feeling sad. Reasoning : (Emotion) (CoE) 2nd Step : The client shows (Cognitive catastrophic cognitive error. Error) Output :'m sorry to hear that. | understand you're feeling extremely upset due to your breakup. It's natural to) feel this way. Breakups are hard but they don't define) your whole existence or future. Let's work through! these emotions and explore ways to cope, Psychotherapy-based CoEs Focus \"Cognitive Error\" \"Emotion Control\" }-â2\"Self-Understanding\" }-â2\"Decision Making\"\n Figure 1: Chain-of-Empathy (CoE) prompting with cognitive reasoning of humanâs emotion based on psychotherapy models. \n# Abstract",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 1 | Recently, there is a growing interest in developing next-generation recommender systems (RSs) based on pretrained large language models (LLMs), fully utilizing their encoded knowledge and reason- ing ability. However, the semantic gap between natural language and recommendation tasks is still not well addressed, leading to multiple issues such as spuriously-correlated user/item descriptors, ineffective language modeling on user/item contents, and ineffi- cient recommendations via auto-regression, etc. In this paper, we propose CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and ID paradigm of RS, aiming to address the above challenges simultaneously. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model the user/item collaborative and content semantics. Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is proposed to effectively learn user/item collaborative/content token embeddings via language modeling on RS-specific corpora estab- lished from user-item interactions and user/item features, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and | 2311.01343#1 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 1,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Recently, there is a growing interest in developing next-generation recommender systems (RSs) based on pretrained large language models (LLMs), fully utilizing their encoded knowledge and reason- ing ability. However, the semantic gap between natural language and recommendation tasks is still not well addressed, leading to multiple issues such as spuriously-correlated user/item descriptors, ineffective language modeling on user/item contents, and ineffi- cient recommendations via auto-regression, etc. In this paper, we propose CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and ID paradigm of RS, aiming to address the above challenges simultaneously. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model the user/item collaborative and content semantics. Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is proposed to effectively learn user/item collaborative/content token embeddings via language modeling on RS-specific corpora estab- lished from user-item interactions and user/item features, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 1 | Figure 1: Chain-of-Empathy (CoE) prompting with cognitive reasoning of humanâs emotion based on psychotherapy models.
# Abstract
We present a novel method, the Chain of Empathy that utilizes insights from psychotherapy to induce Large Language Models (LLMs) to reason about human emotional states. This method is inspired psychotherapy various approachesâCognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy to different patterns of interpreting clientsâ mental states. generated LLMs predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathetic responses aligned with each psychotherapy modelâs different reasoning patterns. The CBT- based CoE resulted in the most balanced responses. The generation of empathetic importance of the findings underscore understanding the emotional context and how it affects human-AI communication. Our research
contributes how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.
# 1. Introduction | 2311.04915#1 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 1,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Figure 1: Chain-of-Empathy (CoE) prompting with cognitive reasoning of humanâs emotion based on psychotherapy models. \n# Abstract\nWe present a novel method, the Chain of Empathy that utilizes insights from psychotherapy to induce Large Language Models (LLMs) to reason about human emotional states. This method is inspired psychotherapy various approachesâCognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy to different patterns of interpreting clientsâ mental states. generated LLMs predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathetic responses aligned with each psychotherapy modelâs different reasoning patterns. The CBT- based CoE resulted in the most balanced responses. The generation of empathetic importance of the findings underscore understanding the emotional context and how it affects human-AI communication. Our research\ncontributes how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.\n# 1. Introduction",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 2 | and user/item features, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and a main text con- sisting of homogeneous item tokens or vocab tokens that facilitates stable and effective language modeling. In addition, a novel mutual regularization strategy is introduced to encourage the CLLM4Rec to capture recommendation-oriented information from user/item contents. Finally, we propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts established from masked user-item interaction history, where rec- ommendations of multiple items can be generated efficiently1. | 2311.01343#2 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 2,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "and user/item features, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and a main text con- sisting of homogeneous item tokens or vocab tokens that facilitates stable and effective language modeling. In addition, a novel mutual regularization strategy is introduced to encourage the CLLM4Rec to capture recommendation-oriented information from user/item contents. Finally, we propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts established from masked user-item interaction history, where rec- ommendations of multiple items can be generated efficiently1.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 2 | contributes how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.
# 1. Introduction
(LLMs) have Large Language Models dramatically generation performance that highly resembles human expressions (Brown et al., 2020; Touvron et al., 2023; Taori et al., 2023; Bommasani et al., 2021). These models have been showcasing their reasoning abilities and achieving high performance in various problem-solving tasks, including professional exams such as the bar exam (Bommarito II and Katz, 2022), a math test (Zhang et al., 2023), and medical diagnoses (Nori et al., 2023). Among many recent findings related to LLMs, one interesting point is the introduction of âChain-of-Thought (CoT)â prompting (Wei et al., 2022; Kojima et al., 2022). This method elicits reasoning before generating outputs. Nevertheless, this recent method has primarily experimented with
logical or arithmetic tasks. Whether reasoning about emotional states or underlying causes enhances empathetic responses to user input remains a relatively under-explored area and merits investigation. Empathetic | 2311.04915#2 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 2,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "contributes how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.\n# 1. Introduction\n(LLMs) have Large Language Models dramatically generation performance that highly resembles human expressions (Brown et al., 2020; Touvron et al., 2023; Taori et al., 2023; Bommasani et al., 2021). These models have been showcasing their reasoning abilities and achieving high performance in various problem-solving tasks, including professional exams such as the bar exam (Bommarito II and Katz, 2022), a math test (Zhang et al., 2023), and medical diagnoses (Nori et al., 2023). Among many recent findings related to LLMs, one interesting point is the introduction of âChain-of-Thought (CoT)â prompting (Wei et al., 2022; Kojima et al., 2022). This method elicits reasoning before generating outputs. Nevertheless, this recent method has primarily experimented with\nlogical or arithmetic tasks. Whether reasoning about emotional states or underlying causes enhances empathetic responses to user input remains a relatively under-explored area and merits investigation. Empathetic",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 3 | # Figure 1: Prospective of developing the next generation of recommender systems based on the pretrained LLMs.
[10], such as GPT [11], T5 [12], LlaMA [13], have demonstrated emergent ability when trained on large-scale corpora [14], show- casing an unprecedented understanding of knowledge and patterns contained in natural language [9, 15]. Consequently, it is promising to develop the next generation of RS based on the pretrained LLMs [16], fully utilizing their encoded knowledge, logical reasoning abil- ity, and generative AI power to understand and reason with the user/item semantics and make more accurate recommendations accordingly, especially when users and items are associated with large amounts of textual features, such as biographies, descriptions, content, reviews, and explanations, etc., in modern online platforms [17, 18]. (see Fig. 1 for an intuitive example of an LLM-based RS)
# 1 INTRODUCTION | 2311.01343#3 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 3,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# Figure 1: Prospective of developing the next generation of recommender systems based on the pretrained LLMs.\n[10], such as GPT [11], T5 [12], LlaMA [13], have demonstrated emergent ability when trained on large-scale corpora [14], show- casing an unprecedented understanding of knowledge and patterns contained in natural language [9, 15]. Consequently, it is promising to develop the next generation of RS based on the pretrained LLMs [16], fully utilizing their encoded knowledge, logical reasoning abil- ity, and generative AI power to understand and reason with the user/item semantics and make more accurate recommendations accordingly, especially when users and items are associated with large amounts of textual features, such as biographies, descriptions, content, reviews, and explanations, etc., in modern online platforms [17, 18]. (see Fig. 1 for an intuitive example of an LLM-based RS)\n# 1 INTRODUCTION",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 3 | logical or arithmetic tasks. Whether reasoning about emotional states or underlying causes enhances empathetic responses to user input remains a relatively under-explored area and merits investigation. Empathetic
requires cognitive reasoning of othersâ mental states. Different psychotherapeutic approaches offer varied perspectives on empathy (Hofmann et al., 2010; Linehan, 1987; Cooper and McLeod, 2011; Wubbolding et al., 2017). By integrating these approaches into LLMsâ reasoning stage, we can enhance the depth and specificity of their empathetic responses. For this purpose, this study delves into these possibilities and proposes a novel prompting, Chain-of- Empathy prompting (CoE). The CoE prompt integrates a text generation. It focuses on clientsâ emotions and the specific factors leading to those emotions, such as cognitive errors, before generating the output.
# 2. Related Work
# 2.1. Theoretical Backgrounds of Empathy | 2311.04915#3 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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"id": "2212.14402"
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"id": "2108.07258"
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"id": "2209.08141"
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"id": "2201.11903"
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"id": "2306.08997"
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"id": "2303.13375"
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"id": "1808.10399"
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 3,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "logical or arithmetic tasks. Whether reasoning about emotional states or underlying causes enhances empathetic responses to user input remains a relatively under-explored area and merits investigation. Empathetic\nrequires cognitive reasoning of othersâ mental states. Different psychotherapeutic approaches offer varied perspectives on empathy (Hofmann et al., 2010; Linehan, 1987; Cooper and McLeod, 2011; Wubbolding et al., 2017). By integrating these approaches into LLMsâ reasoning stage, we can enhance the depth and specificity of their empathetic responses. For this purpose, this study delves into these possibilities and proposes a novel prompting, Chain-of- Empathy prompting (CoE). The CoE prompt integrates a text generation. It focuses on clientsâ emotions and the specific factors leading to those emotions, such as cognitive errors, before generating the output.\n# 2. Related Work\n# 2.1. Theoretical Backgrounds of Empathy",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 4 | # 1 INTRODUCTION
With content growing exponentially on the Web, recommender system (RS) has become an essential component for online service platforms [1]. Nevertheless, since Netflix released its Prize in 2006 [2], RS has long been dominated by the ID-based paradigm, where users and items are represented by unique, continuous ID embed- dings denoting their semantic similarity (e.g., w.r.t. usersâ prefer- ences on items, user/item contents, etc.) [3]. Exemplar ID-based RSs include matrix factorization-based methods such as PMF [4] and the two-tower models [5], where the user/item ID embeddings are either randomly initialized and learned from their historical interactions (i.e., collaborative filtering [6]), or established based on user/item content features (i.e., content-based methods [7, 8]). Recently, large language model (LLM) has become a heated re- search topic that revolutionized both academia and industry [9]. Transformer-based neural networks with billions of parameters | 2311.01343#4 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 4,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 1 INTRODUCTION\nWith content growing exponentially on the Web, recommender system (RS) has become an essential component for online service platforms [1]. Nevertheless, since Netflix released its Prize in 2006 [2], RS has long been dominated by the ID-based paradigm, where users and items are represented by unique, continuous ID embed- dings denoting their semantic similarity (e.g., w.r.t. usersâ prefer- ences on items, user/item contents, etc.) [3]. Exemplar ID-based RSs include matrix factorization-based methods such as PMF [4] and the two-tower models [5], where the user/item ID embeddings are either randomly initialized and learned from their historical interactions (i.e., collaborative filtering [6]), or established based on user/item content features (i.e., content-based methods [7, 8]). Recently, large language model (LLM) has become a heated re- search topic that revolutionized both academia and industry [9]. Transformer-based neural networks with billions of parameters",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 4 | # 2. Related Work
# 2.1. Theoretical Backgrounds of Empathy
Empathy, defined as sharing othersâ emotions and experiences, is a multifaceted concept encompassing cognitive and emotional aspects (Neff, 2003; Anderson and Keltner, 2002; De Vignemont and Singer, 2006; Hall and Schwartz, 2019; Zaki, 2019). Cognitive empathy involves understanding othersâ emotions and perspectives, linked to abilities such as mentalizing and narrative imagination (Eisenberg, 2014). It requires an in-depth cognitive appraisal of the situation, considering factors like pleasantness, control, and certainty of the outcome (Lazarus, 1991; Wondra and (emotional) 2015). Affective Ellsworth, empathy allows to experience individuals othersâ emotions, while motivational empathy, a newer concept, embodies the desire to alleviate othersâ emotional distress (Zaki, 2019).
# 2.2. Empathetic Communication in Text
Natural Language Processing (NLP) has been increasingly developing conversational agents, or chatbots, across various professional domains. These include mental healthcare for victims of crime (Ahn et al., 2020), individuals on the autism spectrum (Diehl et al., 2012), and those suffering from anxiety disorders (Rasouli et al., 2022). | 2311.04915#4 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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"id": "2212.14402"
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"id": "2303.13375"
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 4,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 2. Related Work\n# 2.1. Theoretical Backgrounds of Empathy\nEmpathy, defined as sharing othersâ emotions and experiences, is a multifaceted concept encompassing cognitive and emotional aspects (Neff, 2003; Anderson and Keltner, 2002; De Vignemont and Singer, 2006; Hall and Schwartz, 2019; Zaki, 2019). Cognitive empathy involves understanding othersâ emotions and perspectives, linked to abilities such as mentalizing and narrative imagination (Eisenberg, 2014). It requires an in-depth cognitive appraisal of the situation, considering factors like pleasantness, control, and certainty of the outcome (Lazarus, 1991; Wondra and (emotional) 2015). Affective Ellsworth, empathy allows to experience individuals othersâ emotions, while motivational empathy, a newer concept, embodies the desire to alleviate othersâ emotional distress (Zaki, 2019).\n# 2.2. Empathetic Communication in Text\nNatural Language Processing (NLP) has been increasingly developing conversational agents, or chatbots, across various professional domains. These include mental healthcare for victims of crime (Ahn et al., 2020), individuals on the autism spectrum (Diehl et al., 2012), and those suffering from anxiety disorders (Rasouli et al., 2022).",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 5 | Several preliminary studies have been conducted to investigate the adaptation of LLMs for recommendation systems [19â22]. Typ- ically, these methods can be summarized into two steps: 1) First, instead of representing users/items with continuous ID embeddings, relevant information necessary for reasoning with user interests and generating recommendations, i.e., target user, interacted items, user/item features, and candidate items, are converted into a nat- ural language-based prompt. 2) Then, the prompt is used to query the LLM, where information relevant to recommendations (e.g., whether the user will interact with an item or not) is retrieved from the textual output of the LLM to generate recommendations. The above procedure can be performed in a zero-shot manner [23â26], where the recommendation decisions are obtained directly from the pretrained LLM (e.g., we input all relevant information regarding a user and an item into the chatbox of ChatGPT and ask if the user will interact with the item), or if groundtruths are available, the pretrained LLMs can also be finetuned, such that RS-specific knowledge can be updated into the pretrained model [20, 27â29]. Although progress has been achieved by | 2311.01343#5 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 5,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Several preliminary studies have been conducted to investigate the adaptation of LLMs for recommendation systems [19â22]. Typ- ically, these methods can be summarized into two steps: 1) First, instead of representing users/items with continuous ID embeddings, relevant information necessary for reasoning with user interests and generating recommendations, i.e., target user, interacted items, user/item features, and candidate items, are converted into a nat- ural language-based prompt. 2) Then, the prompt is used to query the LLM, where information relevant to recommendations (e.g., whether the user will interact with an item or not) is retrieved from the textual output of the LLM to generate recommendations. The above procedure can be performed in a zero-shot manner [23â26], where the recommendation decisions are obtained directly from the pretrained LLM (e.g., we input all relevant information regarding a user and an item into the chatbox of ChatGPT and ask if the user will interact with the item), or if groundtruths are available, the pretrained LLMs can also be finetuned, such that RS-specific knowledge can be updated into the pretrained model [20, 27â29]. Although progress has been achieved by",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 5 | Recently, chatbots designed for psychotherapy (e.g., CBT) have shown promising results in assisting the long-term treatment of anxiety and depression (Nwosu et al., 2022). However, current AI-generated responses appear generic and less authentic, making personalized responses a significant challenge. Empathetic reasoning is crucial for these systems, leading to ongoing efforts to enhance their empathetic expression incorporating human-like traits (Roller et al., 2021).
# 2.3. Computational Approach to Empathy
Past research in psychotherapy has primarily focused on empathy based on the analysis of nonverbal cues, such as body language and facial expressions, often requiring manual coding of empathetic responses (Scherer et al., 2001; American Psychiatric Association et al., 1994; Ekman and Friesen, 1971).
Recent advances in artificial intelligence have shifted towards a computational approach, where empathy is predicted from a text corpus and quantified through the labeling of emotions (Rashkin et al., 2019) and distress (Buechel et al., 2018). While most studies have traditionally concentrated on the clientâs capacity for the empathy, counselor is increasingly recognized as critical to successful therapy outcomes (Truax and Carkhuff, 2007). This aspect of expressed empathy is particularly relevant to our approach, where we aim to use LLMs to reflect their understanding of the clientâs needs accurately.
# 2.4. Reasoning in Large Language Models | 2311.04915#5 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
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"id": "2306.08997"
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"id": "2303.13375"
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"id": "1808.10399"
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 5,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Recently, chatbots designed for psychotherapy (e.g., CBT) have shown promising results in assisting the long-term treatment of anxiety and depression (Nwosu et al., 2022). However, current AI-generated responses appear generic and less authentic, making personalized responses a significant challenge. Empathetic reasoning is crucial for these systems, leading to ongoing efforts to enhance their empathetic expression incorporating human-like traits (Roller et al., 2021).\n# 2.3. Computational Approach to Empathy\nPast research in psychotherapy has primarily focused on empathy based on the analysis of nonverbal cues, such as body language and facial expressions, often requiring manual coding of empathetic responses (Scherer et al., 2001; American Psychiatric Association et al., 1994; Ekman and Friesen, 1971).\nRecent advances in artificial intelligence have shifted towards a computational approach, where empathy is predicted from a text corpus and quantified through the labeling of emotions (Rashkin et al., 2019) and distress (Buechel et al., 2018). While most studies have traditionally concentrated on the clientâs capacity for the empathy, counselor is increasingly recognized as critical to successful therapy outcomes (Truax and Carkhuff, 2007). This aspect of expressed empathy is particularly relevant to our approach, where we aim to use LLMs to reflect their understanding of the clientâs needs accurately.\n# 2.4. Reasoning in Large Language Models",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 6 | the pretrained LLMs can also be finetuned, such that RS-specific knowledge can be updated into the pretrained model [20, 27â29]. Although progress has been achieved by these pioneer works, some fundamental dichotomies between natural language process- ing (NLP) and recommendation still remain to be addressed. One main challenge is the gap between natural language and user/item semantics. Generally, there are two strategies to represent user/item | 2311.01343#6 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 6,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "the pretrained LLMs can also be finetuned, such that RS-specific knowledge can be updated into the pretrained model [20, 27â29]. Although progress has been achieved by these pioneer works, some fundamental dichotomies between natural language process- ing (NLP) and recommendation still remain to be addressed. One main challenge is the gap between natural language and user/item semantics. Generally, there are two strategies to represent user/item",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 6 | # 2.4. Reasoning in Large Language Models
Recently, CoT has shown effective in eliciting the reasoning process of the LLMs (Wei et al., 2022; Prystawski et al., 2022; Yao et al., 2023; Kojima et al., 2022). CoT prompting in previous research has included reasoning steps within the prompt instruction for zero- or one- shot learning of LLMs during text generation
CBT-CoE Goal Cognitive reframing Reasoning Tackling negative thought patterns Prompt conditions DBT-CoE PCT-CoE Emotion regulation Addressing emotional dysregulation Self- understanding Enhancing self-awareness RT-CoE Problem-focused coping Identifying cause of the dissatisfaction
Table 1: Comparison of goals and reasoning style in different psychotherapy based CoEs.
(Kojima et al., 2022). This model has improved the performance of problem-solving (Kojima et al., understanding or metaphor (Prystawski et al., 2022), offering new insights and suggesting possibilities for generative models to be used in many other domains.
2011; Knutson and Koch, 2022), and Reality Therapy (RT; Wubbolding et al., 2017)2. Except these promptsâ for instructions were designed the to reflect therapistsâ reasoning process in their respective counseling models.
# 3. The Present Study | 2311.04915#6 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
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"id": "2212.14402"
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"id": "2306.08997"
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"id": "2303.13375"
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"id": "1808.10399"
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 6,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 2.4. Reasoning in Large Language Models\nRecently, CoT has shown effective in eliciting the reasoning process of the LLMs (Wei et al., 2022; Prystawski et al., 2022; Yao et al., 2023; Kojima et al., 2022). CoT prompting in previous research has included reasoning steps within the prompt instruction for zero- or one- shot learning of LLMs during text generation\nCBT-CoE Goal Cognitive reframing Reasoning Tackling negative thought patterns Prompt conditions DBT-CoE PCT-CoE Emotion regulation Addressing emotional dysregulation Self- understanding Enhancing self-awareness RT-CoE Problem-focused coping Identifying cause of the dissatisfaction\nTable 1: Comparison of goals and reasoning style in different psychotherapy based CoEs.\n(Kojima et al., 2022). This model has improved the performance of problem-solving (Kojima et al., understanding or metaphor (Prystawski et al., 2022), offering new insights and suggesting possibilities for generative models to be used in many other domains.\n2011; Knutson and Koch, 2022), and Reality Therapy (RT; Wubbolding et al., 2017)2. Except these promptsâ for instructions were designed the to reflect therapistsâ reasoning process in their respective counseling models.\n# 3. The Present Study",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.04915 | 7 | # 3. The Present Study
We investigated whether eliciting empathetic reasoning in LLMs leads to natural responses. Therefore, we developed CoE prompting to reason emotion and situational factors that could help the model to accurately infer the clientâs emotional experience in mental healthcare and thus choose the most appropriate and context-aware empathetic strategy to communicate.
Models in each prompting condition were tested in zero-shot, with only instructions on which option to choose per class: empathetic strategy (emotional reaction, exploration, and interpretation) and communication level (no expression, weak, and strong) (Sharma et al., 2020). The common reasoning steps involved in each CoE condition were: (1) Identify any word that represents the clientâs emotion, and individual/situational (2) Understand factors that may have led to the expression in the clientâs message.
# 4. Methods
# 4.1. Language Model
# 5. Experiments
We used GPT-3.5 API from OpenAI 1 for system setup. The model (âtext-davinci-003â) temperature was set to 0.9. The top p parameter was set to 1 for nucleus sampling to reduce the randomness of the output (The frequency penalty = 0 and the presence penalty = 0.6).
# 4.2. Chain-of-Empathy Reasoning | 2311.04915#7 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 7,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 3. The Present Study\nWe investigated whether eliciting empathetic reasoning in LLMs leads to natural responses. Therefore, we developed CoE prompting to reason emotion and situational factors that could help the model to accurately infer the clientâs emotional experience in mental healthcare and thus choose the most appropriate and context-aware empathetic strategy to communicate.\nModels in each prompting condition were tested in zero-shot, with only instructions on which option to choose per class: empathetic strategy (emotional reaction, exploration, and interpretation) and communication level (no expression, weak, and strong) (Sharma et al., 2020). The common reasoning steps involved in each CoE condition were: (1) Identify any word that represents the clientâs emotion, and individual/situational (2) Understand factors that may have led to the expression in the clientâs message.\n# 4. Methods\n# 4.1. Language Model\n# 5. Experiments\nWe used GPT-3.5 API from OpenAI 1 for system setup. The model (âtext-davinci-003â) temperature was set to 0.9. The top p parameter was set to 1 for nucleus sampling to reduce the randomness of the output (The frequency penalty = 0 and the presence penalty = 0.6).\n# 4.2. Chain-of-Empathy Reasoning",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 8 | in an LLM-based RS. One strategy is pseudo-ID-based method, where an ID-like word (e.g., "user_ð" or "item_ð") is used to rep- resent the ðth user and ðth item [20]. However, since the vocabu- lary of most LLM contains number-tokens up to two digits, when tokenized, the pseudo ID breaks down into atomic tokens, e.g., "user_4332" into ["user", "_", "43", "32"], where spurious correlations can be introduced for irrelevant users/items (e.g., "user_4332" with "user_43" and "user_32"). In contrast, description-based methods use semantically meaningful descriptions to index users/items, such as item titles [19, 24] or a small amount of newly-introduced tokens assigned to different user/items based on their content similarity [30]. However, description-based methods introduce a strong induc- tive bias on user-item semantic similarity, which may not faithfully capture the true semantics. Introducing user/item ID tokens, un- fortunately, is generally considered infeasible for LLMs, | 2311.01343#8 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 8,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "in an LLM-based RS. One strategy is pseudo-ID-based method, where an ID-like word (e.g., \"user_ð\" or \"item_ð\") is used to rep- resent the ðth user and ðth item [20]. However, since the vocabu- lary of most LLM contains number-tokens up to two digits, when tokenized, the pseudo ID breaks down into atomic tokens, e.g., \"user_4332\" into [\"user\", \"_\", \"43\", \"32\"], where spurious correlations can be introduced for irrelevant users/items (e.g., \"user_4332\" with \"user_43\" and \"user_32\"). In contrast, description-based methods use semantically meaningful descriptions to index users/items, such as item titles [19, 24] or a small amount of newly-introduced tokens assigned to different user/items based on their content similarity [30]. However, description-based methods introduce a strong induc- tive bias on user-item semantic similarity, which may not faithfully capture the true semantics. Introducing user/item ID tokens, un- fortunately, is generally considered infeasible for LLMs,",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 8 | # 4.2. Chain-of-Empathy Reasoning
Table 1 and Figure 1 show four unique prompts with CoE in addition to the base condition (no reasoning): Cognitive-Behavioral Therapy (CBT; Beck, 1979; Kaczkurkin and Foa, 2022; Hofmann et al., 2010), Dialectical Behavior Therapy (DBT; Linehan, 1987), Person- Centered Therapy (PCT; Cooper and McLeod,
We to generate appropriate responses to the posts of seekers seeking advice on Reddit and predict the best suitable empathetic strategy. For the ground- truth label of each empathetic strategy class, we used the EPITOME 3 , crowdsourced Reddit posts of mental health, with an average inter- annotator agreement reported as above 0.68 (Sharma et al., 2020). The dataset comprised pairs of help-seeking posts and responding posts. Each pair was labeled based on (1) the type of expressed âempathy mechanismâ (i.e.,
1 https://openai.com/ 2 We want to emphasize that these descriptions are not exhaustive representations of the goals of each psychotherapy. These goals and reasoning strategies have been specifically modified for LLM prompting
and do not reflect the entire interaction between clinical/counseling psychologists and clients. 3 https://github.com/behavioral-data/ Empathy- Mental-Health | 2311.04915#8 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 8,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 4.2. Chain-of-Empathy Reasoning\nTable 1 and Figure 1 show four unique prompts with CoE in addition to the base condition (no reasoning): Cognitive-Behavioral Therapy (CBT; Beck, 1979; Kaczkurkin and Foa, 2022; Hofmann et al., 2010), Dialectical Behavior Therapy (DBT; Linehan, 1987), Person- Centered Therapy (PCT; Cooper and McLeod,\nWe to generate appropriate responses to the posts of seekers seeking advice on Reddit and predict the best suitable empathetic strategy. For the ground- truth label of each empathetic strategy class, we used the EPITOME 3 , crowdsourced Reddit posts of mental health, with an average inter- annotator agreement reported as above 0.68 (Sharma et al., 2020). The dataset comprised pairs of help-seeking posts and responding posts. Each pair was labeled based on (1) the type of expressed âempathy mechanismâ (i.e.,\n1 https://openai.com/ 2 We want to emphasize that these descriptions are not exhaustive representations of the goals of each psychotherapy. These goals and reasoning strategies have been specifically modified for LLM prompting\nand do not reflect the entire interaction between clinical/counseling psychologists and clients. 3 https://github.com/behavioral-data/ Empathy- Mental-Health",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 9 | which may not faithfully capture the true semantics. Introducing user/item ID tokens, un- fortunately, is generally considered infeasible for LLMs, as directly conducting language modeling on sequences with heterogeneous tokens can be ineffective and unstable, especially when the vocabu- lary of most LLMs is diluted (e.g., â¼ 50k for GPT, and â¼ 30k for T5) by a large number of randomly initialized user/item embeddings. Even if user/item ID token embeddings can be effectively learned via language modeling, another challenge that hinders effective collaborative filtering with LLMs is that, since the order of inter- actions usually does not matter for direct recommendations while human language naturally has an order, spurious temporal cor- relation can be introduced for items placed in different positions when transforming the user historical interactions into textual sen- tences. Furthermore, for content modeling, since pretrained LLMs are not recommendation-oriented, they can easily capture noise in the user/item textual features irrelevant to the recommendation purpose. Finally, since LLMs generate the next token in an autore- gressive manner, recommending multiple items can be inefficient. For both pseudo-ID-based | 2311.01343#9 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 9,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "which may not faithfully capture the true semantics. Introducing user/item ID tokens, un- fortunately, is generally considered infeasible for LLMs, as directly conducting language modeling on sequences with heterogeneous tokens can be ineffective and unstable, especially when the vocabu- lary of most LLMs is diluted (e.g., â¼ 50k for GPT, and â¼ 30k for T5) by a large number of randomly initialized user/item embeddings. Even if user/item ID token embeddings can be effectively learned via language modeling, another challenge that hinders effective collaborative filtering with LLMs is that, since the order of inter- actions usually does not matter for direct recommendations while human language naturally has an order, spurious temporal cor- relation can be introduced for items placed in different positions when transforming the user historical interactions into textual sen- tences. Furthermore, for content modeling, since pretrained LLMs are not recommendation-oriented, they can easily capture noise in the user/item textual features irrelevant to the recommendation purpose. Finally, since LLMs generate the next token in an autore- gressive manner, recommending multiple items can be inefficient. For both pseudo-ID-based",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 9 | and do not reflect the entire interaction between clinical/counseling psychologists and clients. 3 https://github.com/behavioral-data/ Empathy- Mental-Health
Acc Emotional Reaction Interpretation Exploration Base 0.340 Prec. 0.467 Recall 0.185 F1 0.27 Prec. 0 Recall 0 F1 0 Prec. 0.327 Recall F1 0.866 0.475 CBT-CoE 0.319 0.463 0.165 0.244 0.293 0.260 0.276 0.303 0.543 0.389 DBT-CoE 0.334 0.392 0.372 0.382 0.291 0.060 0.100 0.309 0.582 0.404 PCT-CoE 0.336 0.399 0.243 0.302 0.333 0.016 0.031 0.319 0.757 0.449 RT-CoE 0.336 0.407 0.308 0.350 0.354 0.044 0.079 0.309 0.664 0.420
Table 2: Model performance in empathetic strategy classification task by CoE prompting conditions. *Prec. = Precision | 2311.04915#9 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 9,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "and do not reflect the entire interaction between clinical/counseling psychologists and clients. 3 https://github.com/behavioral-data/ Empathy- Mental-Health\nAcc Emotional Reaction Interpretation Exploration Base 0.340 Prec. 0.467 Recall 0.185 F1 0.27 Prec. 0 Recall 0 F1 0 Prec. 0.327 Recall F1 0.866 0.475 CBT-CoE 0.319 0.463 0.165 0.244 0.293 0.260 0.276 0.303 0.543 0.389 DBT-CoE 0.334 0.392 0.372 0.382 0.291 0.060 0.100 0.309 0.582 0.404 PCT-CoE 0.336 0.399 0.243 0.302 0.333 0.016 0.031 0.319 0.757 0.449 RT-CoE 0.336 0.407 0.308 0.350 0.354 0.044 0.079 0.309 0.664 0.420\nTable 2: Model performance in empathetic strategy classification task by CoE prompting conditions. *Prec. = Precision",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.04915 | 10 | Table 2: Model performance in empathetic strategy classification task by CoE prompting conditions. *Prec. = Precision
empathy strategy) and (2) the presence and âlevelâ of each expressed empathy (i.e., communication strength). The three empathy reaction, strategies exploration, with corresponding levels of 0, 1, and 2. Pairs labeled as level 0, indicating no expression of empathy, were excluded. The number of pairs for each strategy was as follows: âemotion reactionâ=1,047, and âinterpretationâ=1,436. We randomly sampled 500 pairs in each emotional reaction and interpretation data to balance the number of pairs between strategies. Each strategyâs final number of pairs was emotional reaction=500, exploration=480, and interpretation=500.
# 5.1. Model Performances
Table 2 and Figure 2 show the performance of the empathetic strategy classification of LLMs with each CoE prompt, measured in terms of precision, recall, F1 score, and accuracy. Upon generating a response, each model with CoE prompts predicted which empathy strategy is most suitable for each seekerâs post among the three strategies. We the predicted empathy strategy with the ground truth calculated strategy prediction accuracy. | 2311.04915#10 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 10,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Table 2: Model performance in empathetic strategy classification task by CoE prompting conditions. *Prec. = Precision\nempathy strategy) and (2) the presence and âlevelâ of each expressed empathy (i.e., communication strength). The three empathy reaction, strategies exploration, with corresponding levels of 0, 1, and 2. Pairs labeled as level 0, indicating no expression of empathy, were excluded. The number of pairs for each strategy was as follows: âemotion reactionâ=1,047, and âinterpretationâ=1,436. We randomly sampled 500 pairs in each emotional reaction and interpretation data to balance the number of pairs between strategies. Each strategyâs final number of pairs was emotional reaction=500, exploration=480, and interpretation=500.\n# 5.1. Model Performances\nTable 2 and Figure 2 show the performance of the empathetic strategy classification of LLMs with each CoE prompt, measured in terms of precision, recall, F1 score, and accuracy. Upon generating a response, each model with CoE prompts predicted which empathy strategy is most suitable for each seekerâs post among the three strategies. We the predicted empathy strategy with the ground truth calculated strategy prediction accuracy.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 11 | To address the above challenges, we present CLLM4Rec, the first method that tightly combines the ID paradigm of RS with the LLM-based paradigm to address the semantic gap. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faith- fully model the user/item collaborative/content semantics, where the embeddings are learned in two stages. The pretraining stage consists of mutually-regularized collaborative and content LLMs that learn user/item token embeddings via language modeling on RS-specific corpora established from user/item interactions and tex- tual features. Specifically, a novel "soft+hard" prompting strategy is proposed for effective language modeling on documents with heterogeneous tokens, where each document is decomposed into a prompt consisting of user/item (soft [31]) and vocab (hard) tokens that describe the contexts and a main text consisting of homoge- neous item tokens (i.e., interaction history) or vocab tokens (i.e., user/item textual features), respectively. Through this strategy, the prediction heads for the two LLMs can focus exclusively on collab- orative and content information, and the stability and effectiveness of language modeling can be substantially enhanced. In addition, a stochastic reordering strategy is proposed for the collaborative LLM to ignore the order of item tokens without negative influence on the vocab tokens. Finally, we propose a novel recommendation-oriented | 2311.01343#11 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 11,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "To address the above challenges, we present CLLM4Rec, the first method that tightly combines the ID paradigm of RS with the LLM-based paradigm to address the semantic gap. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faith- fully model the user/item collaborative/content semantics, where the embeddings are learned in two stages. The pretraining stage consists of mutually-regularized collaborative and content LLMs that learn user/item token embeddings via language modeling on RS-specific corpora established from user/item interactions and tex- tual features. Specifically, a novel \"soft+hard\" prompting strategy is proposed for effective language modeling on documents with heterogeneous tokens, where each document is decomposed into a prompt consisting of user/item (soft [31]) and vocab (hard) tokens that describe the contexts and a main text consisting of homoge- neous item tokens (i.e., interaction history) or vocab tokens (i.e., user/item textual features), respectively. Through this strategy, the prediction heads for the two LLMs can focus exclusively on collab- orative and content information, and the stability and effectiveness of language modeling can be substantially enhanced. In addition, a stochastic reordering strategy is proposed for the collaborative LLM to ignore the order of item tokens without negative influence on the vocab tokens. Finally, we propose a novel recommendation-oriented",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 11 | retrieval (e.g., âNo Empathy Strategyâ). In addition, they sometimes predicted new strategies which did not fall into any of the predefined three strategies (e.g., âReflection,â âValidation: clientâs acknowledging feelings and experiences,â and âApproval: expressing approval or positive reinforcement to the clientâ).
# 6. Qualitative Evaluations
The LLM generally generated courteous and comprehensive responses. While many human peer supporters often provided brief comments and shared personal opinions or give advice, the CoE LLM mostly responded with at least two empathetic strategies and frequently suggested seeking professional help. The model tended to initiate responses by interpreting usersâ current state and subsequent advice or exploring potential options. For example, when a distressed seeker could not control her anxiety after a violent fight between her parents, DBT- responded with multiple CoE prompt empathetic strategies, âIâm so sorry you had to witness that. Itâs understandable that youâre
Outputs with errors in the predicted strategy names were excluded from the analysis. Most of these errors resulted from the nature of LLM as a generative model, which behaves differently from traditional supervised learning models for classification tasks. Despite explicit instructions the models occasionally generated ânoiseâ output and predicted strategies that were not among the provided options. These errors include responses of failed predictions or response | 2311.04915#11 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 11,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "retrieval (e.g., âNo Empathy Strategyâ). In addition, they sometimes predicted new strategies which did not fall into any of the predefined three strategies (e.g., âReflection,â âValidation: clientâs acknowledging feelings and experiences,â and âApproval: expressing approval or positive reinforcement to the clientâ).\n# 6. Qualitative Evaluations\nThe LLM generally generated courteous and comprehensive responses. While many human peer supporters often provided brief comments and shared personal opinions or give advice, the CoE LLM mostly responded with at least two empathetic strategies and frequently suggested seeking professional help. The model tended to initiate responses by interpreting usersâ current state and subsequent advice or exploring potential options. For example, when a distressed seeker could not control her anxiety after a violent fight between her parents, DBT- responded with multiple CoE prompt empathetic strategies, âIâm so sorry you had to witness that. Itâs understandable that youâre\nOutputs with errors in the predicted strategy names were excluded from the analysis. Most of these errors resulted from the nature of LLM as a generative model, which behaves differently from traditional supervised learning models for classification tasks. Despite explicit instructions the models occasionally generated ânoiseâ output and predicted strategies that were not among the provided options. These errors include responses of failed predictions or response",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 12 | Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1
finetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained collabora- tive LLM backbone to predict hold-out items based on soft+hard prompts established from masked usersâ interaction history, where recommendations of multiple items can be generated efficiently. The contribution of this paper can be concretely summarized as: | 2311.01343#12 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 12,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1\nfinetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained collabora- tive LLM backbone to predict hold-out items based on soft+hard prompts established from masked usersâ interaction history, where recommendations of multiple items can be generated efficiently. The contribution of this paper can be concretely summarized as:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 12 | Base 10 og 08 06 06 Fl-score Fl-score 04 04 0.2 emotion reactions interpretation Empathy Strategy 0.0 0.0 exploration PCT CoE 1.0 og 08 0.6 06 Fl-score Fl-score 04 o4 02 emotion_reactions interpretation Empathy Strategy 0.0 0.0 exploration CBT CoE emotion reactions interpretation Empathy Strategy RT CoE DBT CoE 10 08 06 Fl-score 04 emotion reactions interpretation Empathy Strategy 0.0 exploration exploration emotion_reactions interpretation exploration Empathy Strategy
Figure 2: Empathic expression strategy classification accuracy per prompt conditions. Compared to Base condition, CBT-CoE provided the balanced set of each empathy expression but less emotional reaction than other CoEs.
feeling overwhelmed and scared right now. Itâs not okay for anyone to threaten or hurt another person, and itâs not your fault. How can I support you right now?â. This contradicts the original human response in benchmark data: âEverything is wrong with people.â
# 7. Conclusions | 2311.04915#12 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 12,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Base 10 og 08 06 06 Fl-score Fl-score 04 04 0.2 emotion reactions interpretation Empathy Strategy 0.0 0.0 exploration PCT CoE 1.0 og 08 0.6 06 Fl-score Fl-score 04 o4 02 emotion_reactions interpretation Empathy Strategy 0.0 0.0 exploration CBT CoE emotion reactions interpretation Empathy Strategy RT CoE DBT CoE 10 08 06 Fl-score 04 emotion reactions interpretation Empathy Strategy 0.0 exploration exploration emotion_reactions interpretation exploration Empathy Strategy\nFigure 2: Empathic expression strategy classification accuracy per prompt conditions. Compared to Base condition, CBT-CoE provided the balanced set of each empathy expression but less emotional reaction than other CoEs.\nfeeling overwhelmed and scared right now. Itâs not okay for anyone to threaten or hurt another person, and itâs not your fault. How can I support you right now?â. This contradicts the original human response in benchmark data: âEverything is wrong with people.â\n# 7. Conclusions",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 13 | We present CLLM4Rec, the first framework that tightly couples the ID paradigm and LLM paradigm of RS, where encoded knowledge and reasoning ability of LLMs can be fully utilized, while user/item ID token embeddings aligned to the vocab space can well capture intrinsic user interests and item properties. ⢠A novel soft+hard prompting strategy is proposed to pretrain the LLMs on sequences of heterogeneous tokens describing user historical interactions and user/item features via language modeling, where the collaborative and content information can be effectively learned by the user/item token embeddings. ⢠A mutual-regularization strategy is proposed to constrain the CLLM4Rec to learn information more relevant for recommenda- tions from user/item content. In addition, stochastic reordering is proposed such that the order of item tokens can be ignored by the collaborative LLM without influence on the textual parts. ⢠A recommendation-oriented finetuning strategy is proposed for CLLM4Rec, where an item prediction head with multino- mial likelihood is added on the collaborative LLM that predicts hold-out items based on prompt interaction history, where rec- ommendations for multiple items can be generated efficiently.
# 2 RELATED WORK 2.1 Large Language Model (LLM) Basics | 2311.01343#13 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 13,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "We present CLLM4Rec, the first framework that tightly couples the ID paradigm and LLM paradigm of RS, where encoded knowledge and reasoning ability of LLMs can be fully utilized, while user/item ID token embeddings aligned to the vocab space can well capture intrinsic user interests and item properties. ⢠A novel soft+hard prompting strategy is proposed to pretrain the LLMs on sequences of heterogeneous tokens describing user historical interactions and user/item features via language modeling, where the collaborative and content information can be effectively learned by the user/item token embeddings. ⢠A mutual-regularization strategy is proposed to constrain the CLLM4Rec to learn information more relevant for recommenda- tions from user/item content. In addition, stochastic reordering is proposed such that the order of item tokens can be ignored by the collaborative LLM without influence on the textual parts. ⢠A recommendation-oriented finetuning strategy is proposed for CLLM4Rec, where an item prediction head with multino- mial likelihood is added on the collaborative LLM that predicts hold-out items based on prompt interaction history, where rec- ommendations for multiple items can be generated efficiently.\n# 2 RELATED WORK 2.1 Large Language Model (LLM) Basics",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 13 | # 7. Conclusions
In summary, we developed a CoE reasoning prompt for generating empathetic responses based on psychotherapy models, and we the performance of empathetic compared strategy classification. Our findings revealed that LLMs without reasoning showed a significant preference for the exploration strategy, with interpretation being the least preferred strategy. Although all reasoning prompts generated responses most strongly associated with exploration, they differed from the base prompt by generating interpretation to a certain extent. Intriguingly, only the CBT- CoE generated the highest number of the interpretation strategy. This pattern might reflect CBTâs inherent approach - clarifying cognitive errors to clients. These findings incorporating importance of highlight
context-specific therapeutic interactions with generative AIs.
# 8. Limitations and Suggestions
We acknowledge several limitations that should be considered research and development. First, we did not employ more extensive evaluative criteria for empathy, especially those validated from psychology literature like the Interpersonal Reactivity Index (Davis, 1980; Davis, 1983). Future studies should consider evaluating LLMs using their these established scales communication and reproducibility. | 2311.04915#13 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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"id": "2212.14402"
},
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"id": "2205.11916"
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"id": "2108.07258"
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"id": "2306.08997"
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"id": "2303.13375"
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 13,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 7. Conclusions\nIn summary, we developed a CoE reasoning prompt for generating empathetic responses based on psychotherapy models, and we the performance of empathetic compared strategy classification. Our findings revealed that LLMs without reasoning showed a significant preference for the exploration strategy, with interpretation being the least preferred strategy. Although all reasoning prompts generated responses most strongly associated with exploration, they differed from the base prompt by generating interpretation to a certain extent. Intriguingly, only the CBT- CoE generated the highest number of the interpretation strategy. This pattern might reflect CBTâs inherent approach - clarifying cognitive errors to clients. These findings incorporating importance of highlight\ncontext-specific therapeutic interactions with generative AIs.\n# 8. Limitations and Suggestions\nWe acknowledge several limitations that should be considered research and development. First, we did not employ more extensive evaluative criteria for empathy, especially those validated from psychology literature like the Interpersonal Reactivity Index (Davis, 1980; Davis, 1983). Future studies should consider evaluating LLMs using their these established scales communication and reproducibility.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |
2311.01343 | 14 | Transformers with billions of parameters trained on large corpora, i.e., large language models (LLMs), have demonstrated an unprece- dented understanding of natural language and good logical reason- ing ability based on factual knowledge [9]. Based on the part of transformer utilized for language modeling, existing LLMs can be categorized into three classes: encoder-only LLMs, such as BERT [32], encoder-decoder-based LLMs, such as T5 [12], and decoder- only LLMs, such as GPT [11] and LlaMA [13], etc. We focus on LLMs with decoders due to their superior generative abilities compared with the encoder-only models [33]. The training of LLMs is mainly based on two stages. In the pretraining stage, LLMs are trained on large corpora such as website content, Wikipedia, ArXiv paper, and GitHub codes via language modeling (i.e., next/masked token pre- diction), where knowledge in the corpus can be effectively encoded in the weights of the transformer network facilitated by the stacked self-attention modules. Then, during the finetuning stage, exemplar prompt-output pairs (such as questions and answers) or human feedback on multiple generated answers are provided to the LLMs such that they can conduct logical reasoning and generate answers based on the encoded knowledge from the pretrained stage. | 2311.01343#14 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 14,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Transformers with billions of parameters trained on large corpora, i.e., large language models (LLMs), have demonstrated an unprece- dented understanding of natural language and good logical reason- ing ability based on factual knowledge [9]. Based on the part of transformer utilized for language modeling, existing LLMs can be categorized into three classes: encoder-only LLMs, such as BERT [32], encoder-decoder-based LLMs, such as T5 [12], and decoder- only LLMs, such as GPT [11] and LlaMA [13], etc. We focus on LLMs with decoders due to their superior generative abilities compared with the encoder-only models [33]. The training of LLMs is mainly based on two stages. In the pretraining stage, LLMs are trained on large corpora such as website content, Wikipedia, ArXiv paper, and GitHub codes via language modeling (i.e., next/masked token pre- diction), where knowledge in the corpus can be effectively encoded in the weights of the transformer network facilitated by the stacked self-attention modules. Then, during the finetuning stage, exemplar prompt-output pairs (such as questions and answers) or human feedback on multiple generated answers are provided to the LLMs such that they can conduct logical reasoning and generate answers based on the encoded knowledge from the pretrained stage.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} |
2311.04915 | 14 | Our evaluation focused solely on the empathic accuracy of the LLMsâ and did not measure user perception. User perception of empathetic expression varies depending on whether interact with humans or artificially intelligent systems (Medeiros et al., 2021). Furthermore, people perceive and react differently to AIsâ empathetic expressions (Urakami et al., 2019). Thus, future works should investigate how users perceive and
respond to the modelsâ empathetic responses to enhance our understanding of the efficacy of LLMsâ empathetic expressions.
For quantitative evaluation, we used a single LLM model (GPT-3.5) and one domain, mental health. Incorporating a diverse text corpus, and motivational interviewing (Miller and Rollnick, 2012), could enable LLMs to produce more personalized communication. This presents an opportunity for future research to encompass a wider array of topics and conversational styles, thereby increasing the reliability of LLMâs performance. Additionally, different LLMs may excel in varied capabilities, leading each in LLM specific tasks (Sivarajkumar et al., 2023). Investigating and assessing the empathetic expressions generated by different LLMs is crucial for a comprehensive evaluation of LLMsâ ability to discern human emotions and craft appropriate, empathetic responses.
# 9. Ethical Considerations | 2311.04915#14 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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"id": "2212.14402"
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"id": "2303.13375"
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"id": "1808.10399"
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 14,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Our evaluation focused solely on the empathic accuracy of the LLMsâ and did not measure user perception. User perception of empathetic expression varies depending on whether interact with humans or artificially intelligent systems (Medeiros et al., 2021). Furthermore, people perceive and react differently to AIsâ empathetic expressions (Urakami et al., 2019). Thus, future works should investigate how users perceive and\nrespond to the modelsâ empathetic responses to enhance our understanding of the efficacy of LLMsâ empathetic expressions.\nFor quantitative evaluation, we used a single LLM model (GPT-3.5) and one domain, mental health. Incorporating a diverse text corpus, and motivational interviewing (Miller and Rollnick, 2012), could enable LLMs to produce more personalized communication. This presents an opportunity for future research to encompass a wider array of topics and conversational styles, thereby increasing the reliability of LLMâs performance. Additionally, different LLMs may excel in varied capabilities, leading each in LLM specific tasks (Sivarajkumar et al., 2023). Investigating and assessing the empathetic expressions generated by different LLMs is crucial for a comprehensive evaluation of LLMsâ ability to discern human emotions and craft appropriate, empathetic responses.\n# 9. Ethical Considerations",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} |