Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
Authors: Kaihang Pan
Zhaoyu Fan
Juncheng Li
Qifan Yu
Hao Fei
Siliang Tang
Richang Hong
Hanwang Zhang
Qianru Sun
Venue: NeurIPS 2024 spotlight (2024)
Keywords: vision and language
multimodal understanding
Primary area: machine_vision

Abstract: The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied to MLLMs. In this paper, we propose \textbf{UniKE}, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types of knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic levels.  Within such a unified framework, we further promote knowledge collaboration by disentangling the knowledge representations into the semantic and truthfulness spaces. Extensive experiments validate the effectiveness of our method, which ensures that the post-edit MLLM simultaneously maintains excellent reliability, generality, and locality. The code for UniKE is available at https://github.com/beepkh/UniKE.

Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
Kaihang Pan 1., Zhaoyu Fan la Juncheng Li Qifan Yu', Hao Fei? Siliang Tang', Richang Hong", Hanwang Zhang', Qianru Sun
Zhejiang University', National University of Singapore2
Hefei University of Technology", Nanyang Technological University Singapore Management University
(kaihangpan, zyfan, junchengli, yuqifan, siliang] @zju. edu. haofei370nus edu.sg. sg, hongre. hfut@gmail. com hanwangzhangentu. edu. sg, qianrusun@smu, edu.s
Abstract
The swift advancement in Multimodal LLMs (MLLMs) also presents significant challenges for effective knowledge editing. Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses, struggling to balance the desired properties of reliability, generality, and locality when applied MILMs. In this paper, propose UniKE, novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Both types knowledge are conceptualized as vectorized key-value memories, with the corresponding editing processes resembling the assimilation and accommodation phases of human cognition, conducted at the same semantic levels. Within such a unified framework, we further promote knowledge collaboration by disentangling the knowledge representations into the semantic and truthfulness spaces. Extensive experiments validate the effectiveness of our method, which ensures that the post- edit MLLM simultaneously maintains excellent reliability, generality, and locality. The code for UniKE is available https: //g1 thub. com/ beepkh/Unnnie
Introduction
The rapid development of Large Language Models (LLMs) [35, 36] has made it increasingly important ensure the real-time accuracy of their outputs an efficient manner. To this end, in the NLP community, Knowledge Editing [41, 45] has been proposed as data- and time-efficient way to edit LLMs, correcting errors or outdated responses while ensuring no negative impacts are created. The post-edit model is required to generate the desired output given the input (Reliability), also generalize over other equivalent neighbors of inputs (Generality) without altering the output over other irrelevant inputs (Locality). Knowledge editing methods can be divided into two main categories based on the type of knowledge involved: intrinsic knowledge editing [11, 28] where we update specific model parameters store new knowledge in parametric manner; external knowledge resorting [46, 29] that LLMs perceive the new knowledge contained in the relevant context (c.g., via in-context lcarning). Both types of methods have shown good effectiveness in editing LLMs.
Going a step further, with the emergence of advanced multimodal large language models (MLLMs [1 [1]), there has been further exploration into Multimodal Editing. Unfortunately, [4] finds that though
Equal Contribution.
Corresponding Author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

---

Method
Knowledge Type
Knowledge Form
Locality
Generality
Intrinsic Knowledge Editing
Intrinsic Knowledge
Parometric Neurons
4
External Knowledge Resorting
External context) Knowledge
Descriptive Examples
13
UniKE
Intrinsic In-context Knowledge
Unified Vectorized Key-Value Pairs
/
Figure 1: Comparisons of existing knowledge editing methods and UniKE.
Internal vectorized: key value menory
Thansforme
www www Transformer Loyer
(1) Intrin, IE
Intrin,
KE
Goneralty
Locality
Exterral wecforized key-value momory
PPN
Focture
8.8
0.76
shiflin
Self ATTN
Self ATTN
In-context Exomple
0.77
(1) Extern KE
Input Example
Inpur Example
T-Preter KF TPoIE Ours
TPiccher IFF TP Ours
(0) Generality & Locality of Different Methods
(b.1) Vanilla Paradigm of Knowledge Editing
(b.2) Unified Paradign
Figure 2: (a) The generality and locality on MMEdit [4] when applying T-Patcher [11] (intrinsic knowledge editing). IKE [46] (external knowledge resorting). the combination of these two (TP+IKEE, and UniKE for multimodal editing. (b) The paradigm of intrinsic knowledge editing (Intrin. KF) and external knowledge resorting (Extern. KE) before and after knowledge unification.
efficient in editing LLMs, existing methodologies face considerable challenges for MLLMs due to the inherent diversity and complexity of multimodal knowledge. Despite still maintaining high reliability, they struggle to simultaneously achieve both ideal locality and generality, shown in Figure 1.
We argue that both approaches, whether intrinsic knowledge editing or external knowledge resorting, have respective drawbacks for multimodal editing. Specifically, intrinsic knowledge editing (e.g.- T-Patcher [11] that integrates additional neurons into MLLMM) tries to eliminate the risk of losing mreviously-eeanees facts and preserve locality. However, it also leads to the newly integrated knowl- edge resembling rote memorization [3] with weak generality its truthfulness, as multimodal reasoning requires a coordinated understanding of semantics from multiple modalities. Conversely, though external knowledge resorting (e.g., in-context editing [46]) retrieves generalizable informa- tion from external databases, the in-context knowledge may not have strong semantic relevance with the original input [23]. This can mislead the MLLM into areas they originally excelled, resulting in weak locality. Figure 2.a provides direct evidence to support the above discussion.
Therefore, how can we effectively edit MLLMs? One intuitive idea lies directly combining intrinsic knowledge editing with external knowledge resorting, leveraging the advantages of both. However, in intrinsic knowledge editing (such as T-Patcher), the extra integrated knowledge typically incorporates parametric neurons into the model parameters, which is abstract with high-level semantics. Con- versely, external knowledge resorting, such as in-context editing, feeds the MILMM with descriptive images and text at the input end, directly describing the content with low-level semantics. Conse- quently, these two methods exhibit significant differences in paradigms at inconsistent semantic levels and it is challenging to establish synergistic correlation with each other. Figure 2.a demon- strates that simply combining T-Patcher and in-context editing leads to both undesirable locality and generality in the post-edit MLLM, highlighting the drawbacks of each approach separately.
To address the above issue, we propose UniKE, novel multimodal editing method that establishes a unified framework for both intrinsic knowledge editing and external knowledge resorting, enabling a synergistic knowledge collaboration. First, we develop a unified view for intrinsic and external knowledge, both represented as vectorized key-value memories at the same semantic levels. Based on this view, we combine both types of knowledge editing methods, executing them in the latent space with unified paradigm, as shown in Figure 2.b. Specifically, intrinsic knowledge editing integrates extra knowledge into the internal key-value memory at the feed-forward network; external knowledge resorting leverages external key-value memory to inject knowledge into self-attention via feature shifting. Both methods could be performed in the same transformer layers with a synergistic correlation, preliminarily allowing each to utilize strengths for complementing the other.
Moreover, we further effectively enhance the collaboration between intrinsic knowledge and external knowledge resorting. Within the unified framework, the two editing methods still require emphasis on different aspects of knowledge to further complement their respective drawbacks: intrinsic knowledge should focus generalizable truthfulness, while external knowledge should have relevant semantics

---

to the input samples. So we leverage contrastive learning to disentangle the knowledge representations into the semantic and truthfulness spaces, In the semantic space, we enable the intrinsic knowledge to assist in selecting appropriate external knowledge with its inclusion magnitude, preventing the disruption of locality. Simultaneously, in the truthfulness space, we employ the external knowledge to identify a generalizable editing direction to regulate the integrated intrinsic knowledge, alleviating its restriction of generality. Under such a synergistic promotion, extensive experiments show that UniKE achieves promising results under various settings, ensuring that the post-edit MLLM maintains excellent reliability, generality, and locality. Overall, our main contributions are three-fold:
We propose unified paradigm for multimodal knowledge editing, with both intrinsic and external knowledge represented as vectorized key-value memories, conducting at the same semantic levels the same transformer layers.
We disentangle the knowledge representations into the semantic and truthfulness spaces, pro- moting the collaboration between intrinsic knowledge editing and external knowledge resorting.
Our method ensures that, under various backbones and editing scenarios, the post-edit MLLM consistently possesses all three properties well.
2 Related Work
Recent years witness a burgeoning in the techniques of knowledge editing for LLMs [41, 45], with the post-edit model expected exhibit three properties [11]: Reliability, Generality, and Locality (Detailed definitions are given in Appendix B). Knowledge editing methods can be divided into two main categories based on the type of knowledge: intrinsic knowledge editing and external knowledge resorting. Intrinsic knowledge editing [6, 28, 25, 26], involves the parametric storage LLMs' parameters. While external knowledge resorting [46, 29, 47] typically preserves LLMs' parameters and maintains a knowledge database to retrieve relevant cases for each imput with several information retrieval approaches [13, 31]. Overall, intrinsic and external knowledge exhibit significant differences in the knowledge forms (parametric neurons and descriptive in-context examples, respectively).
of knowledge within the model, requiring modifications
Furthermore, the emergence of MLLMS [1, 48, 19, 33, 40, 8] has sparked several studies on multi- modal knowledge editing [4, 30J. However, [4] find that existing methods fall short of expectations when editing MLLMs. Though maintaining high reliability, whether intrinsic knowledge editing or external knowledge resorting, often fails to simultaneously achieve ideal locality and generality as shown in Figure 1. In this paper, we propose a synthesis of both types of methods for multi-modal editing. By unifying intrinsic and in-context knowledge as vectorized key-value memories, we facilitate collaborative interaction between the two within the unified paradigm. fully utilizing the strengths of each method and enabling the post-edit MILLM consistently exhibit all three properties well.
3 Method
In this section, we first develop a unified view for knowledge editing ($3.1). Within the unified framework, we introduce how to realize intrinsic knowledge editing and external knowledge resorting in the latent space ($3.2). Finally, we further enhance the collaboration between both types of knowledge to control the overall editing process ($3.3). The overall framework is shown in Figure 3.
3.1 A Unified View for Knowledge Editing
In our general understanding, intrinsic knowledge editing and external (in-context) knowledge resorting seem to have stark differences. In this section, we will demonstrate that both intrinsic and in-context knowledge can unified as vectorized key-value memories, directly acting on the hidden states within the transformer. Consequently, knowledge editing can be understood as adjusting the key-value pairs in the memory to activate appropriate knowledge for a given query representation.
Intrinsic Knowledge as Internal Key-Value Memory. Previous studies have demonstrated that the feed-forward network (FFN) in the transformer harbors wealth of knowledge [5. 25]. We aim to conduct intrinsic knowledge editing within the FFN and treat FFN as parametric key-value memory
3

---

(o) A Unified View for Knowiedge Editing
(b) Intrinsic and External Knowledge Collaboration
Transfoemer Layer (L+1)
Tranforner Layer (L+3)
Contrastivn
Entrimne wechoriandi koy-volle memory
Learning
Original PEN
Regulating
Original PFN
External vectorizad
Key value nemery
Truthfuiness Encodar
Fecttrreishifingg
Truthfulness Space
Festonejshiifingg
0 ATTN
Self-ATTN
Enoadledge Disontonglieg
ATTN
Self-ATTN
CostSin
Tranformerr Layer (1-1)
Semaniic Faceder
Transformer (1-1)
Ieput Example
Inpot Example
posttive
hidden stanes senaniic hilden stotes
negative hiddinn states
Contrastive
Learning
Semontic Space
semantic represintation
in-contcct knenledge
Figure 3: (a) We develop a unified view for multimodal editing, with both intrinsic and external knowl- edge represented as vectorized key-value memory. (b) We disentangle the knowledge representation into the semantic and truthfulness spaces, further enhancing the knowledge collaboration.
storing within the MLLM. Considering a two-layer FFN: given the input latent states, the FFN treats it as a query with the first FFN layer acting as keys W f K E iddxa" and the second layer as values w://" E Fd xd Here, d is the hidden dimension of the FFN; and d' is the intermediate hidden dimension of the FFN, also interpreted as the memory size. Consequently, FFN effectively uses the query match the keys, with the intermediate hidden state representing the weight for each value in memory. FFN then outputs the weighted sum of all values FFN
Act(gW/"" + +
# Matching keys with query.
(I)
(g) = oW + bif"
# Outputting the weighted aum of values.
where 6/2" E R.,," and 6//" E Rd are two bias vectors. Act(.) is non-linear activation function.
In-context Knowledge as External Key-Value Memory. Traditional in-context knowledge in- creases the context window space and makes it difficult to quantitatively control [27]. Here we propose a similar view of in-context knowledge as an external vectorized key-value memory to establish its connection with intrinsic knowledge. Specifically, in-context learning typically con- catenates the external multimodal knowledge Xknow with the original input sequence Ximput to form the combined sequence X = [Xknow Xinpur]. Considering the self-attention mechanism Att = =X, K = X, V = X) in the transformer, during in-context learning, the attention Attn (X imput, X, X) for tokens in the original input sequence can actually be formulated as follows:
AttnnXinpue, X, X)= Attn (uttn(Xinpur, Xinput, Ximput) +(1 - a) Attn(X (iiputt X Xnnww> X Xknww)
hinyot
bknow
The first term hipput is the original self-attention output without in-context knowledge. The second term is to treat the hidden states Xknow as a key-value memory, the hidden state of Ximput as the query, selectively activating the relevant in-context knowledge henow- Nkvow then performs mosition-wise feature shifting on the original attention output to achieve in-context editing, with a as the scaling factor. We give more complete analysis in Appendix A.
3.2 Unified Knowledge Editing within Latent Space
Assimilation: Intrinsic Knowledge Editing. As intrinsic knowledge considered as key-value memory stored within the FFN, akin to [11], we treat intrinsic knowledge editing as the process of integrating extra knowledge into the internal knowledge memory, thereby establishing connections with prior knowledge. This process is analogous to the Assimilation phase [34] in human cognition, where an individual incorporates new knowledge into their existing cognitive structures. Specifically, based on the analysis in (1), the newly added parametric knowledge is stored in the FFN as key-value pairs (the number of new pairs is ne), transforming the output the FFN as:
To Ouxtvo] Act(g [w
Westra]
W
+ 10
(geenal)
W Westra
(3)
FFNedue(g) = Oestral
+ +6,50 =FF((g) + Oestra We

---

where W E Rd and We E Rueed are the extra keys and values, bg E R is an extra bias vector. Ountra = Act(q W + KK =x represents the activated weight of the extra value. In this way, the newly injected knowledge, in the form of key-value pairs, is seamlessly integrated into the existing knowledge structure of MI.LM.
Accommodation: External Knowledge Resorting. As in-context knowledge can also be vec- torized into hidden states as external key-value memory, we can interpret in-context knowledge editing as shifting the original post-attention latent states as shown in (2), which also allows in-context knowledge to be introduced in a more controlled manner. This process is analogous to the Accommodation phase in human cognition, where individuals modify their existing cognitive schemas to accommodate new information that does not fit into their prior understanding.
Assuming the hidden states of in-context knowledge have been extracted and stored as key-value pairs Meat {(hssmm }. in the input end, the MILM is fed only the original sample without concentrating in-context knowledge. Within a given transformer layer, we initially utilize the pre- attention hidden states brpppue to retrieve the top-K {h K that exhibit the highest cosine
similarity from Meat. obtaining the corresponding { 1 As indicated in E2)( {hpos.s 1 then serves as both keys and values for attention computation. with himpat acting as the query. thereby achieving the in-context latent states hgnow- Subsequently, by simply specifying a scalar a, henow is integrated with the original self-attention output himput, acting as a shifting direction that steers the original states closer to the representations of in-context knowledge, thus facilitating editing.
JKK
Analysis of the Unified Framework. In real life, assimilation and accommodation work together with ongoing interaction to drive cognitive development. Within the unified knowledge editing framework, we also inherently establish a preliminary collaboration between intrinsic knowledge editing and external knowledge resorting: external knowledge assists in storing more generalizable intrinsic knowledge; intrinsic knowledge helps to select appropriate external knowledge. As shown in Figure 3.a. in the 1-th transformer layer, the sott-self-attenton states following in-context editing, directly fed into the FFN for intrinsic knowledge editing. when the FFN input integrates generalizable in-context knowledge, the newly added key-value pairs in FFN also tend to store generalizable knowledge to be better activated. Moreover, the output of the FFN, having just undergone intrinsic knowledge editing, is transmitted the self-attention of the (1 1) layer. Here, it acts as the query to select suitable hidden states of in-context knowledge for in-context editing. Overall, compared to directly combining different knowledge editing methods with various paradigms, we establish synergistic correlation with the unification of knowledge editing paradigm, allowing disferent methods utilize their strengths to complement each other.
3.3 Enhanced Collaboration with Knowledge Disentangling
To further promote the collaboration between intrinsic knowledge editing and external knowledge resorting, it is essential to emphasize different aspects of knowledge: intrinsic knowledge should prioritize generalizable truthfulness to improve generality, whereas external knowledge should main- tain semantic relevance to the input samples to preserve locality. Inspired by this, we extract diverse hidden states for in-context knowledge and innovatively disentangle the knowledge representations into semantic and truthfulness spaces, further enhancing the collaboration within these two spaces.
Extracting In-context Knowledge Representations. To construct the representations of in-context knowledge, we first acquire knowledge that the MI.LM has not previously mastered, and collect triplets [(Qr,Apos Anes))ng)); Q, is the input multimodal question, Apos is the truthful answer, Aneg is the MLLM's hallucinated prediction. For cach piece of knowledge, we pair Q) + Apos as the positive knowledge, Q + Aney as the negative knowledge, and separately pass the positive and negative knowledge through the MLLM, obtaining three critical hidden states. Semantic hidden state heem is related to the last token of the question part before MLLM processes the response, inherently encoding the semantic information on the given examples. Positive hidden state hpos and negative hidden state hneg correspond to the final token of the entire input from the positive and negative knowledge, respectively. They provide insights into how the responses guide the MILM onto the correct or incorrect track. Note that we store (hsem: Apos) as the key-value pairs in the knowledge memory for in-context editing in $3.2. More details are given in Appendix C.
5

---

Disentangling Knowledge Representations. Then we explicitly disentangle the representations of in-context knowledge into semantic and truthfulness spaces. Within the semantic space, Apos and haeg (along with the semantic hidden states hsem) from the same sample encapsulate identical meanings; whereas in the truthfulness space, Npos and Rneg must be distinctly differentiated.
Specifically, we introduce a truthfulness encoder EncTr"(.) and a semantic encoder Enc (-), mapping each pair of {ppos longg} J to the semantic and truthfulness space, deriving a set of semantic representations (H pHporm Heegg) and truthfulness representations pos Hangy) respectively. Within these two latent spaces, we leverage contrastive learning to probe representations with similar truthfulness but different semantics, and conversely, those that are semantically similar but differ in truthfulness. In the truthfulness space, for the given positive negative truthfulness representations hrTr = hopss (h examples sharing the same truthfulness 1f (Hilgg form St, while those with opposite truthfulness Heog (H form S The objective of contrastive learning is to minimize the distance between hTw and S+ while maximizing the distance between h2ru and S
EnERTOr cop(s(hpos.s,)) h))
ENENTEN expls(hnegg h))
Li Tru
Σ
-log EnE(HTor Maay pos.s, h)) -log
EnECHTE Maag expls (h Reg...))) hi) (4)
where s the similarity function. the semantic space, for a given semantic hidden state heem. its corresponding semantic representations (h (hee and heg.)) form the S while those from other examples H Seemmhseen Mopon, H V Reg. form S And the loss of contrastive learning is:
neg
erp(s(heem, hosos))) )) + exp( Ameggg))) Σ -log She eep(s(haene)) h)) + Σne:s 2rp(s(haemsss pos
C2(hsem, L
(5)
meg
Enhanced Knowledge Collaboration within Disentangled Spaces. After knowledge disentan- gling. we could further enhance the knowledge collaboration within the two spaces. Specifically, In the truthfulness space, we calculate the average truthfulness representations (HTTTr and ATT over all positive and negative hidden states of in-context knowledge, to regulate intrinsic knowledge editing. As the representations of positive and negative hidden states exhibit distinct truthfulness after training, we identify an editing direction Ç = -6 - H''' pointing from the center of untruthful representations the center of truthful representations. And then we utilize learnable weight We to map from the truthfulness space back to the representation space: Ç' = WGC. On this basis, during intrinsic knowledge editing in (3), we further combine Wertrr with Ç' as follows:
Wiftn WWeatr + 3- 3-5 ] + 6ft" PP)(4) + Oeztra (W (W + +..5)
FFNedie (4) Dextra]
(6)
where 3 is an editing scalar. In the semantic space, as we leverage in (2) to control the inclusion magnitude of in-context knowledge, we further leverage the hidden states after intrinsic knowledge editing to adaptively control a. Based on henow, hinput in E2),)), representations of the injected in-context knowledge henow Sne (hknow) and the hidden states from the last token of himput (himput[-1 serves a similar role as the semantic hidden state hsem). We then assign the cosine similarity between hgrm and hinputl- [-1] to a with (2) reformulated as:
first extract the semantic EneSem(nsooa))
Attn (X ingutt X, Sim Kenows hingue
[-1]) hinput +
(1 Sii Semm hinguel (-1)))
heeoows (7)
Analysis of Knowledge Collaboration. In the truthfulness space, Ç is derived from the distri- bution deviation between hallucinated knowledge and truthful knowledge based on a large number of examples. As the newly integrated intrinsic knowledge is typically learned from single editing sample which easily leads to overfitting, Ç effectively regulates the values of new intrinsic knowledge into a generalizable truthful direction to improve generality. In the semantic space, when the rele- vance between the in-context knowledge and the input sample is weak, a = Sim - (1) adaptively takes small value thanks to contrastive training. As external knowledge resorting needs to prevent the excessive inclusion of unrelated external knowledge, smaller a effectively reduces its inclusion magnitude to preserve locality. We further provide quantitative analysis in $4.5.

---

Table I: Main results one-step editing the MMEdit. Rel., T-Gen., M-Gen., T-Loc., and M-Loc. refer to Reliability, I-Generality, M-Generality, T-Locality and M-Locality, respectively.
EDITING VQA (E-VOA)
EDITING IMAGE CAPTION (E-IC)
Method
Rel.
F-Gen.
M
T.ac. 1
M.Lo.
Rel. 1
T.Gen.
M.Gen
TLoc.
M-Loc. 1
BLIP-2 OPT
Backhone Model
0.0
0.0
0.0
100.0
100.0
0.0
6.0
0.0
100.0
100.0
(last layor)
58.7
54.2
49.4
67.7
63.1
61.1
52.1
51.6
55.0
49.5
KE
85,3
77.4
75.3
93.8
66.4
50.5
49.0
46.3
95.0
64.3
T- Patcher
83.6
80.3
74.6
90.5
89.7
83.6
73.4
70.0
91.1
82.0
MEND
99.4
98.8
79.1
99.9
96.6
96.1
95.8
74.2
94.5
In Context Editing
99.7
91.9
93.6
48.8
2,5
96.7
78.2
87.6
49.0
3.0
99.4
99.7
95.7
UniKE (Ours)
98.8
98.4
94.8
98.3
96.7
98.3
96.3
93.2
95.8
85.7
MiniGP T-4
Stre: 7.30
0.0
100.0
100.0
0.0
6.0
100.0
100.0
FT (last layer)
70.1
65.7
63.9
72.6
65.8
67.4
65.1
62.8
63.5
52.7
KR
91.8
89.0
60.8
96.9
67.8
96.5
67.8
51.4
97.3
64.4
T-Patcher
85.0
68.2
82.0
83.8
72.3
MEND
98.8
98.6
82.2
98.2
$1.1
96.6
96.1
76.3
98.4
75.3
In-Context Editing
100.0
94.9
90.5
3.7
90.9
81.6
52.2
SERAC
87.7
87.6
85.9
97.5
14.2
91.8
91.4
91.0
97.9
7.2
UniKE (Ours)
98.0
97.4
92.8
98.7
88.8
96.8
95.7
92.4
98.9
87.3
Experiments
We first evaluate UniKE one-step editing ($4.2), the standard setup of multimodal editing. We further extend the setup to sequential editing ($4.3) and cross-task editing ($4.4) for evaluation.
4.1 Experimental Setup
Dataset & Backbone & baselines. Our experiments conducted the MMEdit benchmark [4], which contains two subtasks: Editing VQA (E- (QA) and Editing Image Caption (E-IC). We leverage Reliability, generality (T-Generality and M-Generality) and locality (T-Locality and M-Locality) as the evaluation metrics. For one-step editing, we conduct experiments on BLIP2-OPT [19] and MiniGPT-4 [48]; for sequential editing and cross-task editing, conduct experiments MiniGPT-4.
Furthermore, We use the following baselines: (1) Fine-tuning method: tuning the last layer of MLLM; (2) Intrinsic knowledge editing method: Knowledge Editor (KE) [5], MEND [28]. T-Patcher [[1]: (3) External knowledge resorting method: In-Context Editing (IKE) [46].
SERAC [29].
Implementation Details. We conduct knowledge editing in the latent space. In intrinsic knowledge editing, add extra key-value pairs into the last four transformer layers; In external knowledge resorting, we retrieve top-40 in-context hidden states for each case and conduct feature shifting the last four layers. More details of the experimental setup shown in Appendix D.
4.2
Main Results One-step Editing
Table shows the results of one-step editing, where each edit aims to correct a single mistake.
We further provide a statistical summary in Append D.4. We have the following observations: (i) Most knowledge editing methods could achieve desirable reliability. (ii) Despite achieving high locality, most intrinsic knowledge editing methods have room for improvement in generality (e.g., average generality and locality of T-Patcher across all settings are 71.6 and 87.2). (iii) Although achieving commendable generality, the locality of external knowledge resorting methods is not ideal. Specifically, the average locality (generality) of IKE and SERAC are 26.8 (88.6) and 52.5(91.3). respectively. (iv) Our method effectively balances all three target properties, outperforming the previous SOTA method, MEND. Compared to MEND which transforms the gradients of knowledge editing to a generalizable direction for keeping both locality and generality, UniKE significantly achieves superior locality (93.8 vs. 89.4 average) and generality (95.1 vs. 88.6 average).
7

---

Table 2: Main results sequential editing the MMEdit.
(a) Results 10-step editing on VQA.
(b) Results on 20-step editing VQA.
Mothod
Rel.
T-Gen.
M-Gen
T-Loc.
M-Loc.
Method
Rel.
T-Gen.
M-Gen.
T-Loc.
M-Loc
FT
67.8
614
58.3
66.9
62.3
FT
64.9
59.6
57.9
62.6
61.7
KE
83.2
82.
57.9
84.6
64.3
KE
79.8
74.3
55.7
80.0
60.1
T-Pascher
79.2
62.5
61.4
83.4
79.5
T-Pascher
76.6
56.6
54,8
81,5
78.2
MEND
90.6
79.5
87.4
MEND
85.1
80.4
75.7
82.2
73.9
SFRAC
87.4
84.4
82.7
87.9
12.5
SERAC
87.1
83.0
81.0
85.5
10.7
UBIKE (Ours)
91.3
87.1
85.4
88.9
UnIKE (Ours)
88.9
83.4
81.2
85.7
79.6
(c) Results on 10-step editing on image caption.
(d) Results on 20-step editing on image caption.
Method
T-Gen.
M-Gen.
T-
Rel.
M-Gen.
nc
FT
65.3
63.8
61.9
58.9
49.8
FT
64.0
63.1
60.6
36.4
48.2
KE
84.3
64.2
54.3
90.0
60.1
KE
80.0
58.8
$1.2
84,3
57.9
T-Patcher
80.7
67.5
63.6
89.5
80.6
T-Patcher
76.0
64.1
60.0
88.8
79.8
MEND
MEND
71.4
87.3
SERAC
88.0
87.6
86.7
92.1
6.8
SERAC
87.4
87.0
85.6
90.3
4.8
UniKE (Ours)
91.8
90.4
89.1
93.5
85.0
UniKE (Ours)
88.4
87.2
85.6
90.1
81.1
Table 4: Results of ablation study to illustrate the effect of individual components.
EDITING VQA (E-VQA)
EDITING IMAGE CAPTION (E-IC)
Model
Rel.
T-Gen. 1
M-Gen.
T-Loc. 1
M-Loc.
Rel. f
T-Gen. 1
M-Gin. 1
T-Loe. 1
M-Loc. 1
only
85.3
83.1
85.7
73.3
84.4
only Latent-IKI
94.6
92.2
93.3
54.1
30.5
89.6
85.9
84.4
59.0
36.8
71.8
63.8
50.1
89.9
77.2
Intrin-LatenttIKE
95.9
93.2
89.6
95.2
85.3
96.5
92.4
89.7
95.2
85.5
UniKE (Ours)
98.0
97.4
92.8
98.7
88.8
96.8
95.7
92.4
98.9
87.3
4.3
Main Results on Sequential Editing
In K Ktstep sequential editing, the model is sequentially edited while encountering mistakes in K). After the K'th edit, the post-edit MLLM is utilized to evaluate the target properties. Table 2 shows the results of sequential editing (K = 10, 20; we exclude IKE as its setup in sequential editing is meaningless). It can be observed that (i) whether in editing VQA image captioning tasks, there is a significant decline in the performance of most methods as the number of editing steps (K) increases. Particularly for MEND, while it remains competitive in one-step editing, the results sequential editing are suboptimal.
Deau (
(ii) The performance of external knowledge resorting (SERAC) is minimally affected by the increase in K. However, it inherently suffers from lack of locality. (ili) In contrast, our method consistently maintains superior performance compared to the baselines. It consistently outperforms MEND across all metries of sequential editing, with its advantages over the baseline becoming increasingly significant as K increases.
4.4 Main Results on Cross-task Editing
Cross-task editing builds on the foundation of sequential editing (we select K = 10) and requires the MLLM to simultaneously edit VQA and image-caption samples within the same sequence. Table 3 presents the results of cross-task editing
Table 3: Main results on cross-task editing.
(averaging the results over all E- E-VQA and E-IC samples). is evident that most baselines struggle to effectively edit both tasks within single editing sequence. In contrast, UniKE excels at integrat- ing the knowledge from these two distinct tasks, significantly outperforming baseline methods terms of reliability, generality, and locality.
Method
ReL.
T-Gen.
M-GGen
T-Loc
M-Loc.
FT
65.0
63.2
59.4
57.3
52.2
56.0
85.5
60.2
T-Patcher
79.0
63.2
61.2
84.0
79.8
MEND
75.3
SERAC
87.5
85.0
83.1
90.0
6.6
UniKE
90.7
88.2
86.8
90.4
83,8
4.5 In-Depth Analysis
Effect of Individual Components. We investigate the effectiveness of each component and conduct the following experiments on one-step editing: (1) only Intrin only Latent-IKE: We utilize either intrinsic knowledge editing or external knowledge resorting (Latent IKE), conducting multimodal editing in the latent space. Rows and 2 of Table 4, is evident that single-type knowledge editing approaches cannot simultaneously possess all three properties well, resulting in either generality or locality being unsatisfactory. (2) Intrin IKE: We simply combine intrinsic knowledge editing and vanilla in-context editing without paradigm unification. The results in Row 3 demonstrate that if
8

---

(3)
(5)
Lacalty
70.
Genesity
Locally turatr-aa
Danecilly Ayoars
Localiy I825
Localit nurrerancal
Numborr Contoot Exumples (rkccen Statos
Valie
Tiarmerss
Figure 4: (a) Performance for IKE & Latent-IKE (both combined with intrinsic knowledge editing) with different number of in-context examples or hidden states on E- E-VQA. (b) Performance with different fixed value of and our dynamic a. (c) Performance with different treatments.
Doative ridken toute
Reliability What name this slsthorm
Gemerality What the neme of his plantorm?
Lacality What socid
36
c4
Filling
Dchiv Ediiny
Eidling
St.le Taithen
Theseeal wwt.sllllaaaaeeee.
Afker Kalling
After Editing
Erused Top Twitte
Facebeik
Figure 5: (a) Visualization of different knowledge spaces. (b) A qualitative example.
integrating both types of knowledge editing methods without unified paradigm, it is difficult to fully leverage the individual advantages of each method, still leading to suboptimal generality and locality. (3) Intrin + Latent IKE: We remove the enhanced knowledge collaboration proposed in $3.3. The results in Row 5 validate that the enhanced collaboration based on knowledge representation disentangling further enables the post-edit MLLM to achieve superior generality and locality.
Effect of In-context Editing in Latent Space. To validate the superiority of in-context editing latent space, in Figure 4.a we compare Latent IKE with vanilla IKE across different numbers of in-context samples/hidden-state (both combined with intrinsic knowledge editing without enhanced knowledge collaboration). IKE is criticized that in-context samples are difficult to quantitatively control and take up context window space [27, 23]. It can be observed that in IKE, as the number of in-context samples increases, though generality generally trends upward, there is notable decline in locality. While in method, via quantitatively controlling the inclusion of in-context hidden states while also reducing the prompt length, both generality and locality of the post-edit MLLM show an overall upward trend as the number of in-context samples increases.
Effect of Knowledge Collaboration in Semantic Space. During knowledge collaboration, in semantic space, we adaptively adjust the inclusion magnitude in-context knowledge (assigning the cosine similarity between heem and Dimpue[-1] to a.). To demonstrate the superiority of this strategy, we further experiment with several fixed values for a. As shown in Figure 4.b, the increase the fixed enhances the impact of in-context knowledge, which tends to improve generality. However, it also leads to reduction in locality. In contrast, our method, which adaptively adjusts a based on semantic relevance, customizes an appropriate injection weight for each in-context knowledge. Thereby, ensure an enhancement in generality while also preventing the disruption to locality.
Effect of Knowledge Collaboration in Truthfulness Space. During knowledge collaboration, in the truthfulness space, identify truthful editing direction, Ç to guide intrinsic knowledge editing. To assess the effects of Ç. we conduct the following experiments: (1) w/o Ç: removing the regulation of Ç as per (3)( (2) random Ç: replacing Ç with random tensor; (3) semantic Ç: generating Ç in the same manner within the semantic space. As depicted in Figure 4.c, Ç could further enhance the generalizability of intrinsic knowledge, thus achieving superior editing performance. However, when Ç does not point towards the correct editing direction (random or semantic (). it acts as a disruptor, thereby impairing the editing performance compared to w/o Ç.
Visualization of Different Knowledge Spaces. To give intuitive perspective the disentangled knowledge representations, we employ t-SNE [37] for dimensionality reduction, visualizing the embedding distributions for semantic and truthfulness representations across both positive and

---

negative hidden states. As shown in Figure 5.a, the positive and negative hidden states display similar distributions in the semantic space, yet are distinctly separated in the truthfulness space. This visualization effectively confirms the efficacy of our approach to knowledge disentangling.
Qualitative Examples. As shown in Figure 5.b and Appendix E, UniKE achieves reliable multi- modal editing while generalizing similar scenarios and ensuring accuracy for irrelevant examples.
Conclusion
In this paper, we introduce UniKE, a multimodal editing framework that establishes a unified paradigm for both intrinsic knowledge editing and external knowledge resorting. We conceptualize both types of knowledge as vectorized key-value memories and effectively enhance their collaborations with knowledge disentangling. Extensive experimental results demonstrate that method enhances the post-edit MLLMs across various settings (one-step editing, sequential editing, and cross-task editing), ensuring that they maintain excellent reliability, generality, and locality simultaneously.
Acknowledgements
This work has been supported in part by the Key Research and Development Projects in Zhejiang Province (No. 2024C01 106, 2024C01 102), the NSFC (No. 62272411), the National Key Research and Development Project of China 200111011)))0
References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. 1,3
[2] Dong Chen, Kaihang Pan, Guoming Wang, Yueting Zhuang, and Siliang Tang. Improving vision anomaly detection with the guidance of language modality. arXiv preprint a:0002231000821, 2023. 16
[3] Xiang Chen, Lei Li, Ningyu Zhang, Xiaozhuan Liang, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. Decoupling knowledge from memorization: Retrieval- -aggeeeed prompt learning, 2023. 2
[4] Siyuan Cheng, Bozhong Tian, Qingbin Liu, Xi Chen, Yongheng Wang, Huajun Chen, and Ningyu Zhang. Can we edit multimodal large language models? In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 Conference Empirical Methods in Natural Language Processing, pages 13877-1384 Singapore, December 2023. Association for Computational Linguistics. 2, 3, 7, 16, 17
[5] Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. Knowledge neurons in pretrained transformers. arXiv preprint arXiv:2 104.06696 2021. 3,7
[6] Nicola De Cao, Wilker Aziz, and Ivan Titov. Editing factual knowledge in language models. arXiv preprint N.Kiv:2104.0///1 2021. 3, 17
[7] Hao Fei, Shengqiong Wu, Wei Ji, Hanwang Zhang, Meishan Zhang, Mong-Li Lee, and Wynne Hsu. Video-of-thought: Step-by-step video reasoning from perception to cognition. Proceed- ings of the International Conference Machine Learning, 2024. 16
[8] Hao Fei, Shengqiong Wu, Hanwang Zhang, Tat-Seng Chua, and Shuicheng Yan. Vitron: A unified pixel-level vision 1lm for understanding, generating, segmenting, editing. 2024.
[9] Hao Fei, Shengqiong Wu, Meishan Zhang, Min Zhang, Tat-Seng Chua, and Shuicheng Yan. Enhancing video- languae representations with structural spatio-temporal alignment. IEEE Transactions Pattern Analysis and Machine Intelligence, 2024. 16
10

---

[10] Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, Hongsheng Li, and Yu Qiao. Llama-adapter v2: Parameter-efficient visual instruction model, 2023. 19
[11] Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. Transformer-patchen One mistake worth one neuron. arXiv preprint arXiv:2301.0975,, 2023. 1,2,3, 4, 7, 15, 17
[12] Tao Jin, Wang Lin, Ye Wang, Linjun Li, Xize Cheng, and Zhou Zhao. Rethinking the multimodal correlation of multimodal sequential learning via generalizable attentional results alignment. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguisties (Volume 1: Long Papers), pages 5247-5265, 2024. 16
[13] Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:200..49066, 2020. 3
[14] Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio. Generalization in deep learning. arXiv preprint arXiv:1710.005
1(8), 2017. 15
[15] Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, and Siliang Tang. Fine-grained semantically aligned vision-language pre-training. Advances neural information processing systems, 35:7290-7303, 2022. 16
[16] Juncheng Li, Kaihang Pan, Zhiqi Ge, Minghe Gao, Wei Ji, Wenqiao Zhang, Tat-Seng Chua, Siliang Tang, Hanwang Zhang, and Yueting Zhuang. Fine-tuning multimodal llms to follow zero-shot demonstrative instructions. In The Twelfth International Conference on Learning Representations, 2023. 16
[17] Juncheng Li, Siliang Tang, Linehao Zhu, Wenqiao Zhang, Yi Yang, Tat-Seng Chua, Fei Wu, and Yueting Zhuang. Variational cross-graph reasoning and adaptive structured semantics learning for compositional temporal grounding. IEEE Trunsactions on Pattern Analysis and Machine Intelligence, 45(10): 12611-12617, 2023. 16
[18] Juncheng Li, Junlin Xie, Long Qian, Linchao Zhu, Siliang Tang, Fei Wu, Yi Yang, Yueting Zhuang, and Xin Bric Wang. Compositional temporal grounding with structured variational cross-graph correspondence learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3032-3041, 2022. 16
[19] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-19744 PMLR, 2023. 3, 7, 16, 17
[20] Wang Lin, Jingyuan Chen, Jiaxin Shi, Yichen Zhu, Chen Liang, Junzhong Miao, Tao Jin, Zhou Zhao, Fei Wu, Shuicheng Yan, et al. Non-confusing generation of customized concepts in diffusion models. arXiv preprint MxXiv24405.06114 2024. 16
[21] Wang Lin, Tao Jin, Wenwen Pan, Linjun Li, Xize Cheng, Ye Wang, and Zhou Zhao. Tavt: Towards transferable audio visual text generation. In Proceedings of the 61st Anmual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11983-14999 2023. 16
[22] Wang Lin, Tao Jin, Ye Wang. Wenwen Pan, Linjun Li, Xize Cheng, and Zhou Zhao. Exploring group video captioning with efficient relational approximation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15281-15290 2023. 16
[23] Jiachang Liu, Dinghan Shen, Yizhe Zhang. Bill Dolan, Lawrence Carin, and Weizhu Chen. What makes good in-context examples for gpt-3?, 2021. 2,
[24] Mingxuan Liu, Subhankar Roy, Wenjing Li, Zhun Zhong, Nicu Sebe, and Elisa Ricci. Democ- ratizing fine- ine-grained visual recognition with large language models, 2024. 16
11

---

[25] Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations gpt. Advances in Neural Information Processing Systems, 35:17359-17377
2022. 3
[26] Kevin Meng, Arab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau, Mass- editing memory in transformer. arXiv preprint arXiv:2210.07229, 2022.
[27] Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. Rethinking the role of demonstrations: What makes in-context learning work?, 2022. 4,
[28] Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning. Fast model editing at scale. arXiv preprint arXiv:2110.11309, 2021. 1, 17
[29] Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn. Memory- based model editing scale. In International Conference on Machine Learning, pages 15817-15831. PMLR, 2022. 3, 7.
[30] Haowen Pan, Yixin Cao, Xiaozhi Wang, and Xun Yang. Finding and editing multi- modal neurons in pre-trained transformer. arXiv preprint arXiv:2311.07470, 2023. 3
[31] Kaihang Pan, Juncheng Li, Hongye Song, Hao Fei, Wei Ji, Shuo Zhang, Jun Lin, Xiaozhong Liu, and Siliang Tang. Controlretriever: Harnessing the power of instructions for controllable retrieval. arXiv preprint urXiv:2308. 10025, 2023. 3
[32] Kaihang Pan, Juncheng Li, Hongye Song, Jun Lin, Xiaozhong Liu, and Siliang Tang. Self- supervised meta-prompt learning with meta-gradient regularization for few-shot generalization. arXiv preprint arXiv:2303. /23/4, 2023. 15
[33] Kaihang Pan, Siliang Tang, Juncheng Li, Zhaoyu Fan, Wei Chow, Shuicheng Yan, Tat-Seng Chua, Yueting Zhuang, and Hanwang Zhang. Auto-encoding morph-tokens for multimodal Ilm. arXiv preprint arXiv:2405.01996, 2024. 3
[34] Jean Piaget. Piaget's theory, 1976. 4
[35] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timo- thée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302. 2023. 1
[36] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaci, Nikolay Bashtykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint N.Niv:2307.09288, 2023.
[37] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 988)):257--26M) 2008.
[38] Ye Wang, Wang Lin, Shengyu Zhang, Tao Jin, Linjun Li, Xize Cheng, and Zhou Zhao. Weakly- supervised spoken video grounding via semantic interaction learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume I: Longg Papers),
pages 10914-10932, 2023. 16
[39] Shengqiong Wu, Hao Fei, Xiangtai Li, Jiayi Ji, Hanwang Zhang, Tat-Seng Chua, and Shuicheng Yan. Towards semantic equivalence of tokenization multimodal 11m. arXiv preprint arXiv:2406.05127, 2024. 16
[40] Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. Next-gpt: Any-to-any multimodal 11m. In Proceedings of the International Conference on Machine Learning, 2024.
[41] Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. Editing large language models: Problems, methods, and opportunities. arXiv preprint ar 201. 2023, 1.3
12

---

[42] Qifan Yu, Juncheng Li, Longhui Wei, Liang Pang, Wentao Ye, Bosheng Qin, Siliang Tang. Qi Tian, and Yueting Zhuang. Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, 2024.
[43] Qifan Yu, Juncheng Li, Yu Wu, Siliang Tang, Wei Ji, and Yueting Zhuang. prrompee language model for fine-grained scene graph generation in open world. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023. 16
[44] Qifan Yu, Juncheng Li, Wentao Ye, Siliang Tang, and Yueting Zhuang. Interactive data synthesis for systematic vision adaptation via llms-aiges collaboration. arXiv preprini arXiv:2305.12799 12799, 2023. 16
[45] Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang. Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, et al. A comprehensive study of knowledge editing for large language models. arXiv preprint arXiv:2401.01286, 2024. 1,
[46] Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. Can we edit factual knowledge by in-context learning? In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 Conference Empirical Methods in Natural Language Processing, pages 4862 4876, Singapore, December 2023. Association for Computa- tional Linguistics. 1, 2, 17
[47] Zexuan Zhong, Zhengxuan Wu, Christopher D Manning, Christopher Potts, and Danqi Chen. Mquake: Assessing knowledge editing in language models via multi-hop questions. arXiv preprint arXiv:2305. 14795, 2023. 3
[48] Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: En- hancing dision-languae understanding with advanced large language models. arXiv preprint arXiv:23004. 10592, 2023. 3, 7, 16, 17
[49] Yun Zhu, Jianhao Guo, and Siliang Tang. Sgl-pt: strong graph learner with graph prompt tuning. arXiv preprint arXiv:2302.124499 2023. 16
[50] Yun Zhu, Jianhao Guo, Fei Wu, and Siliang Tang. Rosa: A robust self-aligned framework for node-node graph contrastive learning. arXiv preprint arXiv:2204.13846 2022. 18
[51] Yun Zhu, Yaoke Wang, Haizhou Shi, Zhenshuo Zhang, Dian Jiao, and Siliang Tang. Graphcon- trol: Adding conditional control universal graph pre-trained models for graph domain transfer learning. In Proceedings of the ACM on Web Conference 2024, pages 539-550, 2024. 16
13

---

Appendix
A In-context Knowledge as External Key-Value Memory
In $3.1, we provide brief analysis of how in-context knowledge can be considered as external vectorized key-value memory, and how to conduct in-context editing as feature shifting the latent space. Here, we present more detailed derivation of Eq.(2). Specifically, in-context learning typically concatenates the external multimodal knowledge X Kknow with the original input sequence Xinput to form the combined sequence X [X [Xinour: Ximpue]. Considering the self-attention mechanism Attn Q, K, V) softmax (QW, W WWW,, in the transformer (Wa. W q. W. denote the learnable key, query, and value matrices, respectively), the in-context knowledge simply changes the attention module through prepending context matrix before the original input examples. During in-context learning, the attention Att = Xinput, K = X, V = X) for tokens in the original input sequence can actually be formulated as follows:
Aun(XXmput, X, X)
AttnpXipput, [Kknow Kinput], [Kknows Ximpuel)
Softmax (XimpueW, Wq (Xknno Wk, Ximpue We) 7
(Know W. Xinpue)
Softmax (Xmppu W₂ WT X(npue) X Xinpue We + (1 a) Softmax (Xipput W, Xhnow) Xanow W.
a Attm( Xempue, Ximput, Xinpue) + (1 a). Attn( (itpuu, Xanow, Xknow)
hinput + (1 a. hknow,
(8)
Here a is a scalar that represents the sum of normalized attention weights between in-context knowledge and the original input:
E, exp([iippu Wg WT X(nput)
Σ, exp(Xinpus W W. X Xxnow): + Ej exp(Xinput W.W W. X impuel
where Timput is the token within the original input sequence. We can find that in-context learning actually applies a position-wise modification to the original attention output by shifting the original output features, with the self-artention controlling the shift direction and a controlling the shift distance. Therefore, in this paper, dynamically adjust the value of to control the inclusion magnitude of in-context knowledge representations.
B
Task Definition
B.1
Multimodal Knowledge Editing
The goal of multimodal knowledge editing is to efficiently modify initial MLLM's behavior based on a specific edit descriptor, without incurring significant retraining costs or affecting its behavior other unrelated samples. Formally, (Ve, Ze, Me) E Dedit is the edit descriptor, where Ue refers to the visual input, Te refers to the textual input, Ye denotes the desired output. And we represent the MLLM (with its parameters denoted 8) function f : (V, X) - Y that maps the multimodal input (v, xr) to its corresponding prediction yo = fo(v, (U, x). For intrinsic knowledge editing, the parameters of the post-edit MLLM frost are updated to 0. ((ve, Ze. Fe. 3e)): while for external knowledge resorting methods, the MILM's parameters remain 0, and additional relevant external knowledge K. is incorporated adjust the MI.LM's output on the input (Ve, xx) from Yo to ye- Additionally. there is broad set of inputs closely associated with the edit descriptor, referred to the editing neighbor (V,,, 2.)). ). The MLLM's behavior should also be corrected for examples within this neighbor while maintaining its performance for nfeegh-r examples unaltered:
the extra input. On this basis, successful edit should first
poost (v,x) (o)(v. or f0(
Ye
(w, x) E ( Re) (v, & (
(10)
yo
Based on the above analysis, there are often three metrics used to measure the performance of the post-edit MLLM: reliability, locality, and generality.
14

---

Knowledge editing is reliable when the post-edit MLLM successfully changes pre- diction from yo ye [11]. We access the reliability based on the average accuracy of the edit case:
Reliability.
Men Be Doat (10
(11)
Locality. Knowledge editing should be implemented locally, ensuring that the post-edit MILM should not change the output of irrelevant out-of-neighbor examples. And we leverage two metrics to evaluate locality: M.eert (T-Locality) and Mises (M-Locality). For T-Locality, the visual modules of MLLM, and leverage rudimentary question-ana aantoo--nn--nnwweee datasets Loo-T = {1r2, We)) examine whether the MILM's understanding of pure textual input remains unaffected.
remove
Text E (te. Mel Pedi
[1 (
(12)
Of course, we also need consider the potential ramifications of knowledge editing on visual comprehension. Given Loc-MM = {(um, Lm. F. M-Locality is measured by the rate at which the post-edit MLLM maintains the same predictions the pre-edit MLLM on multimodal input.
Mlng Img
[1 Fie Fe =o))
(13)
Generality. It not sufficient for knowledge editing to merely correct individual erroneous inputs. The post-edit MLLM should also generalize to equivalent neighbors with strong generalization [14, 32]. In multimodal scenarios, equivalent neighbors can be rephrased textual sentences or rephrased images, corresponding to the metrics of T-Generality and M-Generality, respectively. And then generality is assessed by the average accuracy on examples uniformly sampled from these equivalent neighbors (N( or ((ve))
M gen
E (veiseme)-Pal
[1 (tee
(14)
((r))
M
B De (omma)))) (vr)~A((v))
(15)
B.2 Sequential Editing
Previous multi-modal editing focuses on one-step editing, addressing single error from one target sample at time, and subsequently evaluating the three metrics based on the sample itself, its equivalent neighbors, and its out-of out-of-neighhor examples. However, one-step editing is not applicable to practical situations. Following [11], we extend multimodal editing into the setup of sequential editing. In K-step sequential editing, we have set of target samples Deeq to be edited in a sequence ( (Dreqy K; each sample in Ding also contains its own equivalent neighbors and out-of-neighbor examples). After continuously editing all K target samples, we obtain post-edit MLLM fo (Etr. (m.ra)))
We first evaluate the reliability, locality, and generality of the post-edit MLLM within the sequence in a manner similar. And the final performance of the above metrics will be averaged over all Dang ~ Dedit-
M'ret Deali
1
Σ
(16)
K
(DENTe:De))
M Teat
Bay Delii
1
Σ
"(rii (E ((aw)))=
(17)
K
Mlmm EaDiyDDail
Σ
1 (f(miimmtle (E (v...))) (lmm
(18)
K
(cm 2mm Diness
M = EDaa gen
Σ
(v.0,0)))
(19)
(De Prap (xr))
M!lig EDu
Σ
I Is(unaaeee (( (Iionnns (0.z.))) (o))
(20)
K
Ne) (vv)))((ve))
15

---

B.3 Cross-Task Editing
On the basis of sequential editing, we further allow the K target samples within a sequence come from different tasks. For example, single sequence might include samples from both the VQA task and the image caption task. This requires the MLLM integrate knowledge from distinct tasks to achieve cross-task editing. The evaluation for the above metrics is similar to that of sequential editing. However, instead of separately reporting metries for each task, we mix the data from different tasks and calculate a single set of (Reliability, T-Locality, M-Locality, T-Generality, M-Generality) for the mixed data. This provides measure of the post-edit MLLM's average performance across different tasks.
C Preparing In-context Knowledge for Representation Extraction
To extract representations of in-context knowledge, we should first prepare a set of in-context
knowledge for the MILM. In multimodal scenarios [18, 17, 44, 2, 9, 51, 49, 39, 21, 20, 22, 12, 38, 7J, We aim to provide in-context knowledge that the MLLM has not previously mas- tered, which means the MLLM cannot provide correct answers to the corresponding questions. And we attribute MLLMs' hallucinated re- sponses to deficiencies three levels, shown in Figure 6: (1) Insufficient vision extraction. MLLMs first utilizes Visual Prompt Generator (VPG, e.g., Qformer [19]) to abstract image fea- tures [48]. However, some necessary reasoning- aware visual details that complement the pri- mary content and semantically connect the text instructions, may be ignored and not extracted by the VPG [16, 15]. (2) Inaccurate vision recognition. Even if the VPG extracts suffi- cient visual features for reasoning, the MLLM might fail to understand the corresponding vi- sual objects if has not been sufficiently ex- posed to these feature patterns during training, resulting in inaccurate recognition of visual ob- jects [24, 43]. (3) Incorrect text-vision col- laborative reasoning. Even with sufficient vi- sion extraction and accurate vision recognition, MLLMs may struggle with understanding the spatial relationships between different visual regions. Moreover, they may exhibit errors in common- sense knowledge when combining vision with text instructions, ultimately resulting in incorrect text-vision collaborative reasoning [42].
Image
Irouff extraction
MILLAee Fecur
What comine fovends us the dirrncs
Right Rad
Ohyeche
Level It: Inaccurate vision recogattion
Qusstoor kind of oxina Hopp
Whong Sparton
Right Oul
MLLIME Focus
collsberetive
Queatiay Whick spcuer clul does player cormanlyy play
Saleh German
Right Trtte Wans CF
We attribute MLLMs' hallucinated re- sponses to deficiencies at three levels.
Figure 6:
On this basis, we collect a substantial of hallucinated predictions from MLLMs across these three levels to develop in-context knowledge. For each multimodal question Q, with MLLMs hallucinated predictions Aneg. we additionally provide a truthful answer Agos For each piece of knowledge, we pair Q₂ + Aneg as the negative knowledge and Q₂ + Apos as the positive knowledge. For example, assuming [IMG] is an image of Lionel Messi, "[IMG] Question: Which soccer club this player currently playing for? Answer: Paris Saint-Germain. represents the negative knowledge; while "[IMG] Question: Which soccer club this player currently playing for? Answer: Inter Miami." represents the positive knowledge. Subsequently, stated in $3.3, we extract several critical hidden states for the collected in-context knowledge.
D Experimental Details
D.1 Dataset
We conduct experiments on the MMEdit benchmark [4], which consists of two sub-tasks: Editing VQA (E-VQA) and Editing Image Caption (E-IC). We leverage Reliability, locality (T-Locality and M-Locality), and Generality (T-Generalitt and M-Generality) as the evaluation metrics. The definitions of each metric are given in the previous section (Appendix B). We leverage BLIP2-
16

---

Table 5: Average results of one-step sequential editing across various sub-tasks, backbones, and sub-metrics (locality and generality, with &-Locality&M-LLL --oallity and T-Locality&T G-Gnerllltty as the specific sub-metrics). The best result is marked bold. The second best result is underlined.
ONE-STUP EDITING
SEQUENTIAL EDITING
Methods
Reliablity
Generality
Locality
Reliability
Generality
Locality
64.3
58.1
61.2
63.5
61.0
58.4
KE
81.1
65.4
72.4
81.8
62.3
72.7
84.5
78.1
MEND
98.6
88.6
89.4
87.7
80.0
80.3
IKE
96.8
88.6
26.8
SERAC
94.7
91.3
52.5
87.5
84.8
48.8
UniKE (Ours)
98.0
95.1
93.8
90.2
86.2
85.9
OPT [19] and MiniGPT-4 [48] as the backbone models, which are under BSD 3-Clause License, And the MMEdit benchmark is under MIT license.
Moreover, the original setup of MMEdit only involves one-step editing, where each edit aims to correct a single mistake from a single target sample, and the above metrics are assessed after each edit. We further extend the setup sequential editing and tross-task editing, both of which are defined in the previous section. For sequential editing in E- VQA and E-IC, we select K = 10 and K 20 as the editing steps within sequence. For eross-task editing, choose K 10, with each editing sequence containing 5 E-VQA samples and 5 E-IC samples. Additionally, we no longer report the results for E-IC and E-VQA separately in cross-task editing; instead, we present the average results of all E-IC and E-VQA samples.
D.2 Baselines
Fine-tuning is the most widely employed strategy for adapting pre-trained language models to specific tasks. So we leverage vanilla fine-tuning as the baseline for multimodal editing. We only tune the last layer of MLLM, which has been verified as the most effective fine-tuning strategy in [4].
Fine-tune.
Knowledge Editor (KE). KE [6] is intrinsic knowledge editing method that corrects erroneous knowledge language models without re-training the whole model. leverages a hypernetwork (a bidirectionalLLSTM)) to predict the weight update for constrained optimization.
MEND, Model Editor Networks with Gradient Decomposition (MEND [28]) is also method of intrinsic knowledge editing. It learns to transform the editing gradients into a generalizable direction via employing a low-rank decomposition of gradients, aiming to keep both generality and locality during knowledge editing.
T-Patcher. T-Patcher [11] also a typical intrinsic knowledge editing method that integrates addition neurons for addressing mistakes in the last several layers of the (FFN) within language models.
Network
In-context Knowledge Editing (IKE). IKE [46] edits the language model by prompting the model with several retrieved edit demonstrations from the external database. As result, the language model can generate outputs that align with the provided knowledge when given refined knowledge context prompt.
SERAC. SERAC [29] is also a method of external knowledge resorting. It leverages an explicit memory system cache edits, which is later utilized to adjust the output of the language model during inference. Moreover, the memory system employs a small auxiliary scope classifier to determine whether the input falls within the scope of the memory cache.
D.3 Implementation Details
We conduct knowledge editing in the latent space with unified paradigm. In intrinsic knowledge editing, we add extra 10 key-value pairs in the FFN of the last four transformer layers; for external
17

---

Reliability
Generality
Locality
What the picture?
What is
speed of this?
What's on the readP
(a)
Before Editing
Before Editiin
Before Editting
This is train.
usually rurd 100 160 lons per hour
motorcycle
Editing
After Editing
Editing
old- fashioned from
The old fashioned from runs
motorcycle
50 70 kms hour
Reliability
Generality name of social platform?
Locality
What name of this social
f
What is the this social
platform now?
plotform
(b)
Before Editing
Before Eddtiing
Before Editiing
Twitter
The social plotform of Elon Musk is Twitter.
Facebook.
After Editing
After Editing
After Editing
It used be called Twitter, now it's called X.
to Twitttor. now it's colledd
Facebook
Reliability
Generality What for company's mobile phones?!
Locality
What the latest version of this OS?
G
What is the latest version of this
company's OS?
(c)
Before Editing
Before Edicing
Refore Editting
Windows II.
After Editing
After Editing
After Editing
Android
Windows 11
Reliability
Generality
Locality
Why is the lion chasing the ont elope?
Why doees't 't the lion chase the bear?
What is the person doing?
(d)
Before Editing
Before Editing
Before Editing
The antelope entered the lien's s territory
Becouse: they together.
Feeding dog.
After Editing
After Editing
After Editting
smaller and slower pray.
Lions usually
Lions usually choose smaller and slower
Feeding a dog.
Reliability
Generality
Locality
does this animal feed on?
Why seagull fly to this person?
What this animal usually eat?
Editing
Before Editing
Editing
Seagulls eat fish.
The seagull looks happy
Grass, lezves, fruits and other plants
After Editing
After Editing
Afler Editing
Seagulls look for ice cream
The seagull wants sat the ios cream
Grass, loaves, fruits and other plants
Figure 7: Qualitative examples of multimodal editing.
knowledge resorting, we retrieve top-40 hidden states of in-context knowledge with the highest similarity for each case and conduct feature shifting for in-context editing in the last four transformer layers. To extract the hidden states of in-context knowledge, we collect more than 15K triplets. Furthermore, during knowledge disentangling, both the truthfulness encoder and the semantic encoder simply consist of several ML. layers. We leverage the collected in-context knowledge representations to pre-train the encoders via contrastive learning [50]. During contrastive learning, both encoders optimized using the Adam optimizer with a learning rate of le-4. After that, completing single
18

---

one-step edit takes only a matter of seconds and we run all experiments with 6 NVIDIA RTX A6000 GPUs.
D.4
Statistical Summary of Results
In Table 5, we present statistical summary of the average reliability, locality, and generality of the post-edit MLLM for both one-step and sequential editing. Although some methods (e.g., MEND) achieve reliable editing in multimodal scenarios, UniKE effectively balances all three target properties, significantly outperforming baseline methods in terms of generality and locality.
Qualitative Examples
In Figure 7, we show more qualitative examples. In case (a), after altering the MLLM's cognitive bias of particular concept through knowledge editing, we enable the MILM to leverage its existing world knowledge to automatically correct other related details associated with that concept. In cases (b) and (c), after updating outdated knowledge through UniKE, the MLLM can activate this updated knowledge to correctly answer series of related questions. In cases (d) and (c), model editing enables the MLLM to look beyond the superficial aspects and answer questions from deeper perspective. Furthermore, for all five cases, ensure the locality of the post-edit MILM without altering its original responses to irrelevant inputs.
Limitations
There still exist some limitations in work: (1) At present, we have only considered editing MLLMs with visual comprehension capabilities, and have not addressed editing in visual generation scenarios. (2) Due to the resource limitation, we do afford to edit MLLMs with larger number of parameters such as the 65B LLaMA Adapter V2 [10].
G Broader Impacts
Ethical Impacts. This study does not raise any ethical concerns. The research does not involve subjective assessments or the use of private data. Only publicly available datasets and models are utilized for experimentation.
Expected Societal Implications. This study proposes a data- and time-efficient way to edit MLLMs. major societal concern with this technology lies in potential for misuse. For example, some malicious individuals may exploit our technology to fabricate false information for knowledge editing and spread rumors. To counter these threats, is crucial to develop strong ethical standards and implement ongoing surveillance.
19

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The main claims made in the abstract and introduction accurately reflect the paper's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer:
[Yes]
Justification:
We discuss the limitations of the work in Appendix F.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-texx system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
Justification:
We give the proof in Appendix A.
20

---

Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental Result Reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Answer: [Yes]
Justification: We give experimental setup and implementation details in Section 4.1 and Appendix D.
Guidelines:
The answer NA means that the paper does include experiments.
If the paper includes experiments, a No answer to this question will not perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, provide access the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how replicate the results, access a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, or other means that are appropriate the research performed.
While NeurIPS does not require releasing code, the conference does require submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g.. with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors welcome deseribe the particular way they provide for reproducibility. the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
21

---

The codes are provided in supplemental materials (also in the anonymous repository). The benchmark data comes from the open-source dataset, MMEdit. Guidelines:
Justification:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.. cc/
public/guided httpg/ggiieeeeoeeeuuuieeeeeeeeee
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results, See the NeurIPS code and data submission guidelines (https: //nps..co wttpccccccccccccctccccccccccccct guwdee/iee//// wewdddesummsssiooooollooo
for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing much information possible in supplemental material (appended the paper) is recommended, but including URLs to data and code permitted.
6. Experimental Setting/eetails
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Based on the original test settings of MMEdit, we have provided necessary implementation details of our method in Appendix D.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core the paper to a level of detail that is necessary appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment Statistical Significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer:
[Yes]
Justification: To ensure a fair comparison with the baseline method, we strictly adhere to the testing settings of the MMEdit dataset, including leveraging consistent random seed defined MMEdit during test.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the enror bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
22

---

It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments Compute Resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
We give the statements of experiments compute resources in Appendix D.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported the paper (e.g., preliminary or failed experiments that didn't 't make it into the paper).
9. Code Of Ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: https::/ //tpprip//////....... cc/ cccppublc//eeeecccceecceeceeeeee Cppublc/Ethicsuuut ines?
Answer: [Yes]
Justification: The research conducted in the paper conform, every respect, follows the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., if there special consid- eration due to laws or regulations in their jurisdiction..
10. Broader Impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification:
We discuss the Broader Impacts in Appendix G.
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to
23

---

any negative applications, the authors should point it out. For example, it is legitimate point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how system lears from feedback over time, improving the efficiency and accessibility ML).
11. Safeguards
Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Answer: [NA]
Justification:
The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g.. code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: We include the license of each asset in Appendix D.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. cww datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New Assets

---

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: The paper does not release new assets.
Guidelines:
The answer NA means that the paper does release new assets.
Researchers should communicate the details the dataset/code/moodll as part their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and Research with Human Subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
25


Reviews:
[{"review_id": "paUYQjZshI", "reviewer": "Reviewer_mp35", "summary": "This paper proposes UniKE, a novel multimodal editing method that establishes a unified perspective and paradigm for intrinsic knowledge editing and external knowledge resorting. Within such a unified framework, the authors further promote knowledge collaboration by disentangling the knowledge representations into the semantic and truthfulness spaces. Extensive experiments validate the effectiveness of UniKE, which ensures that the post-edit MLLM simultaneously maintains excellent reliability, generality, and locality.", "questions": "Please refer to the weakness below.", "limitations": "The paper briefly mentioned limitations.", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written and easy to follow and the motivation is clear and reasonable.\n2. The paper gives a unified perspective on the intrinsic refinement of knowledge and the strategic reorganization of external knowledge, which can enhance subsequent research endeavors.\n3. The experimental settings (one-step editing, sequential editing, cross-task editing) are fair.", "weaknesses": "1. The authors only edit Qformer style MLLMs (MiniGPT4, BLIP2), more foundation models should be compared. e.g., LLava. Besides, the improvement on BLIP2 (Tab.1) seems incremental.\n2. More recent model editing baselines should be compared to prove the effectiveness of the proposed methods.\n3. The paper focuses on MLLM editing, what are the main differences between MLLM editing and LLM editing? What are the specific designs for multimodal models? I notice that some compared methods are proposed for LLM editing. Can the proposed methods be used on LLM editing? The authors should give some explanations and experimental results if possible.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for the valuable comments! We are encouraged to see that our work can enhance subsequent research endeavors. We will address your concerns point by point.\n\n**Q1**: More foundation models should be compared (LLava). The improvement on BLIP2 seems incremental.\n\n**A1**: Thank you for raising the important concern. We first leverage LLaVA1.5 to perform multimodal editing, and then we will show that the improvement on BLIP-2 OPT is not incremental.\n\n**(1.1)Multimodal Editing on LLaVA**\n\nWe conduct both one-step editing and 10-step cross-task editing on LLaVA1.5. The results are shown in **Tables 1 and 2 of the rebuttal PDF**. With LLaVA as the backbone, **UniKE still balances all three target properties well, outperforming all baseline methods** in both one-step editing and cross-task editing. This demonstrates that UniKE is **model-agnostic and effective across various types of MLLMs**.\n\n***[Please Refer to Tables 1 and 2 in the Rebuttal PDF for the Results]***\n\n&nbsp;\n\n**(1.2)Analysis on BLIP-2 OPT**\n\nThough improvement of one-step editing on Blip-2 OPT(in Table1 of our paper) may seem incremental, we aim to demonstrate the significant performance advantage of UniKE on BLIP-2 OPT from 3 aspects:\n\n(I) Across all ten metrics of BLIP-2 OPT one-step editing, UniKE achieves best results in five metrics, with the remaining suboptimal results only marginally lower than the best. We also calculate the average performance of each method across all ten metrics, as shown in the following Table. **The average result of UniKE in BLIP-2 OPT one-step editing significantly surpasses other methods, exceeding the second-best method (MEND) by 5.1 points**.\n\nTable: Avg performance of all metrics on Blip-2 OPT one-step editing \n|Method|FT|KE|TPatcher|MEND|IKE|SERAC|**UniKE**\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-: \n|AVG Perf.|56.2|70.3|82.3|90.5|65.3|77.6|**95.6**|\n\n(II) In one-step editing on BLIP-2 OPT, **UniKE is the only method that consistently performs well across all metrics, whereas other methods have at least one notably poor metric**. For instance, MEND's M-Gen(E-VQA), M-Gen(E-IC), and M-Loc(E-IC) results are 15.7, 19.0, and 14.9 points lower than UniKE's, respectively. IKE and SERAC perform especially poorly on M-Loc, with accuracy below 10%.\n\n(III) Additionally, the relatively low difficulty of one-step editing task may allow some baselines to maintain relatively high evaluation scores. In more challenging scenarios such as multi-step cross-task editing, UniKE's advantage becomes more apparent. Specifically, in **BLIP-2 OPT cross-task editing** with higher difficulty (shown in  **Table 3 of the Rebuttal PDF**), **UniKE consistently outperforms all other baselines in all metrics, demonstrating a more significant performance advantage**.\n\n***[Please Refer to Table 3 in the Rebuttal PDF for the Results]***\n\n&nbsp;\n\n**Q2**: More recent baselines should be compared.\n\n**A2**: Thank you for the suggestion. We further compare UniKE with two recent baselines proposed in 2024: MENMET, WISE (a concurrent work). We leverage MiniGPT4 to conduct both one-step editing and cross-task editing. The results are shown in **Tables 4 and 5 of the Rebuttal PDF**. **The performance of UniKE surpasses these recent baselines, further demonstrating the effectiveness of UniKE.**\n\n[Tan et al 2024]MENMET: Massive Editing for Large Language Models via Meta Learning.ICLR2024.\n\n[Wang et al 2024]WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models.23 May 2024.\n\n***[Please Refer to Tables 4 and 5 in the Rebuttal PDF for the Results]***\n\n&nbsp;\n\n**Q3**: What are the main differences between MLLM editing and LLM editing?What are the specific designs for multimodal models?Can UniKE be used on LLM editing?\n\n**A3**: Thank you for your questions. We will address them from three points:\n\n**(3.1)** The main differences between MLLM editing and LLM editing lie in **task difficulty**. LLM stores single-modality NLP knowledge within its parameters, while MLLM stores multi-modality knowledge, making MLLM editing more challenging due to the need to edit knowledge from multiple modalities to fix errors. **Although mainstream editing methods proposed for both are similar, current methods can effectively edit LLMs but not MLLMs** [Chen et al 2023].\n\n**(3.2)** [Chen et al 2023] attempted specific designs for editing MLLMs such as **editing the unique Qformer**, but found **these methods less effective than directly applying LLM editing methods**, as shown in the following table. This is because the bottleneck for MLLMs lies in enabling LLMs to reason with multimodal input, rather than in extracting visual information. Therefore, **mainstream MLLM editing methods still focus on editing LLM and directly adopt LLM editing techniques.**.\n\nHowever, directly applying LLM editing methods for multimodal editing still fails to balance both generality and locality. In this paper, we discuss the shortcomings of existing LLM editing methods when applied to multimodal scenarios and develop a unified framework along with knowledge disentangling, which leverages the strength and mitigates the weakness of each method type, leading to more effective multimodal editing.\n\n||Methods editing Qformer|Methods editing LLM|UniKE\n|:-|:-:|:-:|:-: \n|AVG Multimodal Editing Performance|71.8|89.3|**95.2**|\n\n**(3.3)** Since LLM editing is merely a simpler case of MLLM editing, UniKE which successfully addresses MLLM editing, can also be effectively used for LLM editing. Following previous LLM-editing work, we use GPT-J-6B to conduct both one-step and multi-step editing on the ZsRE benchmark. As shown in **Table 6 of the Rebuttal PDF, UniKE still outperforms all baselines in LLM editing**.\n\n***[Please Refer to Table 6 in the Rebuttal PDF for the Results]***\n\n&nbsp;\n\nWe will integrate these experiments into our paper. Thank you again for the valuable feedback!\n\n[Chen et al 2023]Can We Edit Multimodal Large Language Models?EMNLP2023.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ecBYC8D6hw", "reviewer": "Reviewer_w8aR", "summary": "UniKE is a unified framework for multi-modal knowledge editing that includes three main aspects:\n1.Knowledge Separation: UniKE divides knowledge into factuality and semantic spaces to manage and coordinate different types of knowledge more effectively.\n2.Knowledge Collaboration: In the factuality space, UniKE standardizes new knowledge based on a learned factuality distribution, enhancing reliability and generality. In the semantic space, it adjusts the integration of external knowledge based on relevance to the input samples, maintaining locality.\n3.Multi-step Editing: UniKE supports single-step, multi-step sequence, and cross-task editing while maintaining high reliability, generality, and locality.\nThrough these innovations, UniKE significantly improves performance in multi-modal knowledge editing tasks.", "questions": "Please address the comments in the weaknesses section.", "limitations": "The limitations of the article have been discussed by the authors", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The paper introduces UniKE, a novel framework that seamlessly integrates intrinsic and external knowledge editing, enhancing the model’s ability to handle complex multimodal information effectively. The motivation behind the work is clear, and the article is well structured, guiding the reader through the innovative approach and its benefits.\n\n2.By disentangling knowledge into semantic and truthfulness spaces, the proposed method ensures robust collaboration between different types of knowledge, significantly improving the model's reliability, generality, and locality. The method's effectiveness is demonstrated through comprehensive experiments across various settings, consistently outperforming existing state-of-the-art methods.\n\n3.UniKE's design allows for application across different multimodal models and editing scenarios, making it a versatile and robust solution for enhancing multimodal language models.", "weaknesses": "1.The paper does compare UniKE with other intrinsic knowledge editing and external knowledge resorting methods and highlights its efficiency in several aspects. However, it lacks a detailed discussion on computational speed and resource utilization.\n\n2.The reliance on fine-tuning for knowledge updates could lead to overfitting, especially if the model is frequently updated. This could impact the model’s generalization abilities, making it less effective in unforeseen or less frequent scenarios.\n\n3.The paper lacks detailed explanations for the evaluation metrics in Line 240. In Table 2, across multiple experiments, it is unclear why there is little difference compared to the SERAC method in the T-Loc metric, but a significant difference in the M-Loc metric.\n\n4.The paper lacks detailed information about the parameter \"n\" used in the contrastive learning formula, making it difficult to understand its impact on model performance, and does not discuss how different values of \"n\" might influence the results and effectiveness of the method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your kind feedback and valuable comments. We will explain your concern as follows.\n\n**Q1:** Lacking a detailed discussion on computational speed and resource utilization.\n\n**A1:** Thank you for the suggestion. In **Table 7 of the Rebuttal PDF**, we list the computational speed and resource utilization of each method. As can be seen, compared to most baselines, **UniKE achieves significant performance advantages without incurring additional resource costs or computation time**.  Moreover, although external knowledge editing methods (IKE, SERAC) are faster in terms of editing speed, their performance is unacceptable, **lagging 20-30 points behind UniKE**. These results demonstrate the superiority of UniKE.\n\n***[Please Refer to Table 7 in the Rebuttal PDF for the Results]***\n\n&nbsp;\n\n**Q2:** The reliance on fine-tuning for knowledge updates could lead to overfitting.\n\n**A2:** First, The objective and evaluation metrics of knowledge editing include not only successfully completing edits but also ensuring the post-edit MLLM can generalize over equivalent input neighbors (**Generality**) and maintain consistent output for irrelevant inputs (**Locality**). In fact, **Generality and Locality can be considered as two aspects of evaluating overfitting: the new knowledge should generalize well without disrupting the generalization of unrelated old knowledge.**\n\nIn our method, to maintain Generality and Locality and prevent overfitting, **we retain the original model parameters while combining both intrinsic and external knowledge editing within a unified framework**. Intrinsic knowledge editing **introduces a minimal amount of tunable parameters** in a small subset of FFNs **to preserve locality**. Meanwhile, external knowledge editing incorporates retrieved in-context representations (with no tunable parameters) into the latent space of the transformer **to enhance generality**.  We also propose knowledge disentangling to promote knowledge collaboration.\nIn the semantic space, intrinsic knowledge helps select appropriate external knowledge, **preventing locality disruption**. In the truthfulness space, external knowledge identifies a generalizable editing direction, regulating intrinsic knowledge and **alleviating its restriction on generality**.\n\nThe experimental results (Tables 2, 3, and 4 in our paper) demonstrate that UniKE achieves better locality and generality in various multimodal editing scenarios, **and more effectively addresses the issue of overfitting compared to baselines.**\n\n&nbsp;\n\n**Q3:** The paper lacks detailed explanations for the evaluation metrics. In Table 2, across multiple experiments, it is unclear why there is little difference compared to the SERAC method in the T-Loc metric, but a significant difference in the M-Loc metric.\n\n**A3:** We apologize for any confusion regarding the evaluation metrics. In Appendix B.1, we detail the definitions of the five evaluation metrics (see Eq.11-15). \nIn brief, these metrics assess accuracy across different input samples: Reliability on edit targets, T-Locality on unrelated QA tasks, M-Locality on unrelated VQA tasks, T-Generality on samples that rephrase the text inputs based on the edit targets, and M-Generality on samples that redraw image inputs based on the edit targets.\n\nFurthermore, To analyze the reasons for the little differences in the T-Loc metric and the significant differences in M-Loc, we examined some intermediate outputs of SERAC during editing. We discover that the reason is **related to the pipeline of SERAC**.\nSERAC adopts a counterfactual model while keeping the original model unchanged. It employs a scope classifier to determine if new input falls within the range of stored edit examples. If the input matches any cached edit, output the counterfactual model’s prediction based on the input and the most probable edit. Otherwise, the original model’s prediction is given.\n\nFor T-Loc, the input samples contain only text information, which differs significantly from the input format of edited samples. This makes the counterfactual model less likely to activate, resulting in outputting the original model's prediction and better locality. However, for M-Loc, the input samples, like the edited samples, contain multimodal information. As the scope classifier is trained to activate the counterfactual model for multimodal inputs even if they are unrelated to the editing target, **the incorrect choices made by the scope classifier result in significantly worse M-Loc performance of SERAC, causing the observed phenomenon.**\n\n&nbsp;\n\n**Q4:** The paper lacks detailed information about the parameter \"n\" used in the contrastive learning formula, and does not discuss how different values of \"n\" might influence the results and effectiveness of the method.\n\n**A4:** Thank you for raising an important concern! \"n\" is a crucial hyperparameter worth discussing. First, n represents the number of training samples used for contrastive learning during encoder pre-training. These training data come from the knowledge representations of the in-context knowledge we constructed, which we detailed in Appendix C on how to prepare and extract these in-context knowledge representations. \nIn our experiments, we set n to approximately 16000.\n\nBuilding on this, we perform an ablation study on the value of n to test how different numbers of pre-training samples affect the final editing performance. As shown in **Figure 2 in the Rebuttal PDF**, the performance keeps increasing when the data number increases from 0 to 8000. Beyond this, escalating the data count from 8000 to 16000  yields only marginal enhancement. **It demonstrates that our encoder training is data-efficient and only requires relatively small amounts of data to achieve effective knowledge representation disentangling.**\n\n***[Please Refer to Figure 2 in the Rebuttal PDF for the Results]***\n\n&nbsp;\n\nThank you once again for your time and valuable feedback!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nsDrtI8LiW", "reviewer": "Reviewer_V1xr", "summary": "This paper proposes UniKE, a novel multimodal editing method that establishes a unified perspective for intrinsic knowledge editing and external knowledge resorting. On this basis, the authors combine both types of knowledge editing methods, executing them in the latent space with a unified paradigm. Furthermore, this paper proposes to disentangle the knowledge representations into the semantic and truthfulness spaces, effectively enhancing the collaboration between intrinsic knowledge and external knowledge resorting. Extensive experimental results show that UniKE achieves promising results under various settings, ensuring that the post-edit MLLM maintains excellent reliability, generality, and locality.", "questions": "See the weaknesses.", "limitations": "The authors have adequately discussed the limitations and potential negative societal impact.", "rating": 9, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "(1) I think knowledge editing for MLLMs is a relatively new topic. Previously, a few studies merely adapted existing knowledge editing methods from the NLP field into multimodal domain. To the best of my knowledge, this paper is the first to conduct a detailed and systematic analysis of the strengths and weaknesses of existing methods when applied to editing multimodal LLMs.\n\n(2) The proposed method is very novel and effective. Previous efforts in knowledge editing show significant differences between intrinsic knowledge editing methods and external knowledge resorting methods. In this work, the authors ingeniously convert in-context editing into the format of feature shifting, achieving a unification of the editing paradigms that can operate simultaneously within the same transformer layer with synergistic correlation. I find this to be a very inspiring design. Moreover, the design of knowledge collaboration is closely integrated with this unified paradigm.\n\n(3) The experiments are very solid and thorough, clearly demonstrating that UniKE effectively addresses multimodal knowledge editing tasks under various setups. Meanwhile, the authors have also provided the implementation code for the experiments.\n\n(4) I commend the authors for conducting an extensive set of ablations and analyses, which are very helpful in understanding the impact of each component within UniKE.\n\n(5) Additionally, I believe that the method proposed by the authors is not only applicable to knowledge editing tasks. By converting in-context learning into the representation space and avoiding the need to increase the context window space, it better synergizes with parameter update learning. I consider this to have significant implications for further studies on how to construct more powerful MLLMs.", "weaknesses": "(1) In the NLP community, some studies will discuss the resilience to overediting [1] of knowledge editing methods by adopting the contrastive knowledge assessment [2] (CKA). Unlike the locality property that measures whether LLMs forget previous knowledge, overediting can be understood as excessive generalized to seemingly similar but unrelated samples. Although there may be no current work on multimodal editing that discusses the phenomenon of overediting, I encourage authors to add relevant experiments for a straightforward comparison of the resilience to overediting among each method (UniKE, MEND, T-Patcher, and IKE).\n(2) A more challenging task of knowledge editing is counterfactual editing, where the edited answer $y$ to the question $x$ can sometimes be counterfactual to the real world. A typical counterfactual editing dataset in the NLP community is called COUNTERFACT [3], which more accurately reflects the true effectiveness of knowledge editing methods by avoiding the effects of LLMs knowing this knowledge before editing. I encourage authors to construct multimodal counterfactual editing datasets and conduct more experiments to verify whether UniKE performs better in counterfactual editing scenarios compared to MEND, T-Patcher and IKE. \n[1] Zheng, Ce, et al. \"Can we edit factual knowledge by in-context learning?.\" arXiv preprint arXiv:2305.12740 (2023).\n[2] Dong, Qingxiu, et al. \"Calibrating factual knowledge in pretrained language models.\" arXiv preprint arXiv:2210.03329 (2022).\n[3] Meng, Kevin, et al. \"Locating and editing factual associations in GPT.\" Advances in Neural Information Processing Systems 35 (2022): 17359-17372.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your valuable comments and high appreciation of our work! We are encouraged that our research is recognized as having significant implications for further studies on constructing more powerful MLLMs. We will address your concerns point by point.\n\n**Q1:** In the NLP community, some studies will discuss the resilience to overediting of knowledge editing methods by adopting the contrastive knowledge assessment (CKA). Unlike the locality property that measures whether LLMs forget previous knowledge, overediting can be understood as excessively generalized to seemingly similar but unrelated samples. Although there may be no current work on multimodal editing that discusses the phenomenon of overediting, I encourage authors to add relevant experiments for a straightforward comparison of the resilience to overediting among each method (UniKE, MEND, T-Patcher, and IKE). \n\n**A1:** Thank you for raising such a professional question! To construct the dataset for overediting evaluation, we randomly select a portion of the test data from the E-VQA task. We then make minor modifications to the text questions in each example so that they appear similar to the original questions but differ in semantics, thus constructing the test data for overediting. To improve experimental efficiency, we conduct experiments on MiniGPT-4 with the setup of one-step editing. The results of the CKA evaluation for each method are listed in the following table. We can see that UniKE outperforms all other methods. The results indicate that **UniKE has less influence on over-editing, which further demonstrates its robust capabilities.\n\nTable 1: The results of CKA evaluation on MiniGPT-4 with the setup of one-step editing. \n| Method | T-Patcher | MEND | IKE |  UniKE |\n|:-|:-:|:-:|:-:|:-:  \n| **CKA** | 1.38 | 1.33| 1.47  | **1.58** |\n\nWe also present the results in Table 8 of the Rebuttal PDF.\n\n&nbsp;\n\n**Q2:** A more challenging task of knowledge editing is counterfactual editing, where the edited answer to the question can sometimes be counterfactual to the real world. A typical counterfactual editing dataset in the NLP community is called COUNTERFACT, which more accurately reflects the true effectiveness of knowledge editing methods by avoiding the effects of LLMs knowing this knowledge before editing. I encourage authors to construct multimodal counterfactual editing datasets and conduct more experiments to verify whether UniKE performs better in counterfactual editing scenarios compared to MEND, T-Patcher, and IKE.\n\n**A2:** Thank you for the insightful question. We first construct a counterfactual editing dataset based on the MMEdit dataset, ensuring that the MLLM did not have prior knowledge of the editing target. We also conduct experiments on MiniGPT-4 with one-step editing. The results of the counterfactual editing are shown in the following table. **It can be seen that UniKE significantly outperforms existing methods in this more challenging editing scenario, fully demonstrating the effectiveness of our method.\n\nTable 2: Performance of counterfactual editing on MiniGPT-4.\n| Method | Rel | T-Gen | M-Gen | T-Loc | M-Loc | Avg |\n| :-   | :-:|:-:|:-:|:-:|:-:|:-:| \n| T-Patcher|  80.0 | 65.9 | 57.7 | 84.2 | 88.3 | 75.2 |\n| MEND | 90.6 | 83.2 | 74.1 | 93.5 | 82.1 | 84.7 |\n| IKE | 90.3 | 83.7 | **81.5** | 44.1 | 5.0 | 60.9 |\n| UniKE  | **90.8** | **84.7** | 80.7 | **94.9** | **94.5** | **89.1** |  \n\nWe also present the results in Table 9 of the Rebuttal PDF.\n\n&nbsp;\n\nWe will add these experiments to the main body or the appendix of our paper. Thank you once again for your insightful and professional feedback!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1TfaH7AXzQ", "reviewer": "Reviewer_4G2a", "summary": "The paper introduces UniKE, a novel multimodal editing method that addresses challenges in knowledge editing for Multimodal Large Language Models. UniKE unifies intrinsic knowledge editing and external knowledge resorting by vectorized key-value memories. By disentangling knowledge representations into semantic and truthfulness spaces, UniKE promotes collaboration between intrinsic and external knowledge editing, enhancing the post-edit MLLM's reliability, generality, and locality. Extensive experiments validate UniKE's effectiveness.", "questions": "Why would you add extra 10 key-value pairs in the FFN of the last four transformer layers in intrinsic knowledge editing?\nIt seems that \\zeta is quite sensitive. Could you explain that?", "limitations": "N/A", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1.\tUniKE establishes a unified framework for intrinsic knowledge editing and external knowledge resorting.\n2.\tExtensive experiments demonstrate that UniKE consistently maintains excellent reliability, generality, and locality", "weaknesses": "1.\tThe presentation is confusing. For example, in intrinsic knowledge editing, how do you actual edit the intrinsic knowledge in FFN. Do you follow the same pipeline as T-Patcher or adding an additional neural network? Although intrinsic knowledge is considered as key-value pairs but they are different in the end.\n2.\tThe unified framework and disentanglement of knowledge representations into semantic and truthfulness spaces introduces additional complexity to the editing process. If I understand correctly from the implementation details, it requires more than 15k additional triplets to train the encoders.\n3.\tIn the experiments, I think /beta is another hyperparameter, but I did not find an experiment discussing it.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your constructive comments. We will explain your concerns point by point.\n\n**Q1:** The presentation is confusing. For example, in intrinsic knowledge editing, how do you actual edit the intrinsic knowledge in FFN? \n\n**A1:** We apologize if our manuscript causes any confusion. **The intrinsic knowledge editing is implemented by adding 10 knowledge neurons in the FFN of the last four transformer layers** with a similar pipeline as T-patcher. \nThe addition of neurons can be understood as an expansion of the dimension of the weight matrix in the FFN, as shown in Eq.3. \nThis process retains the original $d\\times d'$\nweights in the FFN while appending a learnable parameter of dimensions $d\\times ne$ as the new neurons. \nAnd Eq.2 demonstrates why intrinsic knowledge can be viewed as key-value pairs in this context.\n\nAlthough this pipeline may seem similar to TPatcher, **the key innovation of our paper lies in consistently transforming in-context editing to the latent level and integrating it into the self-attention**, which allows both external and intrinsic knowledge editing to **follow a unified paradigm within a cohesive framework**. Furthermore, **our intrinsic knowledge editing is actually an improvement of T-Patcher**. By synergistically unifying the paradigms of both editing methods and incorporating knowledge disentangling, we identify a truthful editing direction, $\\zeta$, which **further guides intrinsic knowledge into a more generalizable direction**. To understand how $\\zeta$ enhances intrinsic knowledge editing, please refer to Eq.6, which is inspired by the success of [Chen et al 2022].\n\n[Chen et al 2022]AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition.Neurips2022.\n\n&nbsp;\n\n**Q2:** The unified framework and knowledge disentanglement introduce additional complexity to the editing.\n\n**A2:** Thank you for valuable feedback. We would like to address your concerns from two points.\n\n**(2.1)** The unified framework and knowledge disentanglement almost **incur no additional time or GPU memory overhead** during the editing process. Both the truthfulness and semantic encoders consist of several MLP layers **with only 4M parameters** (MLLM contains 7B parameters), which **remain frozen** during editing. As shown in **Table 10 in the Rebuttal PDF, these encoders enhance editing performance with NEGLIGIBLE additional time and memory overhead**. \nYou can also refer to **Table 7 in the Rebuttal PDF** to compare the editing speed and performance of UniKE with baselines. It shows that UniKE achieves significant performance advantages without incurring additional resource costs or computation time. \nTherefore, we think that the additional complexity they bring to the editing process is acceptable.\n\n**(2.2)** Leveraging 15k additional triplets to train the encoders is a **one-time** pre-training process, taking less than an hour. Once pre-trained, the encoders are **frozen** for downstream knowledge editing tasks, **adding no training-time cost to knowledge editing.** As shown in **Table 10 in the Rebuttal PDF**, after removal of encoders without pre-training, the performance significantly decreases. So we think that the time cost of this one-time pre-training is acceptable. Furthermore, this pre-training is common in knowledge editing. Methods like MEND and SERAC also require pre-training, **with pre-training time often exceeding one day, which is 24 times longer than  UniKE pre-training.**\n\n***[Please Refer to Table 10 in the Rebuttal PDF for the Results]***\n\n&nbsp;\n\n**Q3:** Did not find an experiment discussing /beta\n\n**A3:** We apologize for neglecting to discuss the value of $\\beta$. In our experiments, we set $\\beta$=1.0. And we conduct further experiments, ranging $\\beta$ from 0.0 to 1.6 to evaluate the performance. As shown in **Figure 1 in the Rebuttal PDF**, within the range of 0.8 to 1.6, UniKE's performance shows no significant variation with changes in $\\beta$. **This indicates that $\\zeta$ is robust to the change of $\\beta$**.\n\n***[Please Refer to Figure 1 in the Rebuttal PDF for the Results]***\n\n&nbsp;\n\n**Q4:** Why would you add extra 10 kv pairs in the FFN of last 4 transformer layers in intrinsic knowledge editing? It seems that \\zeta is quite sensitive.\n\n**A4:** Thank you for the question. We would like to address your concerns from two points.\n\n**(4.1)** The implementation detail for intrinsic knowledge editing is to add an extra 10 kv pairs to the FFN of the last four transformer layers. \"extra\" means that **we preserve the original parameters of the MLLM while achieving intrinsic knowledge editing through the additionally added kv pairs**. Moreover, We choose these hyperparameters because **we find them to be the most suitable** for multimodal editing. Of course, we maintain a comparable number of neurons and transformer layers in the T-Patcher baseline for fair comparison.\n\n**(4.2)** Figure 4(c) in our paper highlights the importance of selecting an appropriate method for constructing the editing direction $\\zeta$. Specifically, using a random vector for $\\zeta$ degrades performance due to the introduction of noise during editing, which hinders the MLLM from learning effectively. In contrast, our proposed method for constructing $\\zeta$ shows a noticeable performance improvement (especially in generality), as we learn a generalizable editing direction from a large number of samples, addressing the inherent limitations of generality in intrinsic knowledge editing. \n\nMoreover, once completing the construction of $\\zeta$, as seen from the above **A3**, adjusting its weight $\\beta$ has little impact on editing performance. \nIt indicates that **the constructed $\\zeta$ itself is robust to weight variations, although its construction method is sensitive.**\n\n&nbsp;\n\nOnce again, we express our heartfelt gratitude for your valuable suggestions! In the revised version of the paper, we will improve the descriptions to make them clearer.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We sincerely thank all the reviewers for their insightful and valuable comments! Overall, we are encouraged that they find that:

(1) The motivation is **clear and reasonable**, supported by a well-structured article. *(Reviewer mp35, Reviewer w8aR, Reviewer V1xr)*

(2) UniKE establishes **a unified framework** for intrinsic knowledge editing and external knowledge resorting, which is **novel and effective**. *(All Reviewers)*

(3) The experiments are **solid and thorough**, clearly demonstrating that UniKE consistently maintains excellent reliability, generality, and locality **across various settings**. *(All Reviewers)*

(4) UniKE has **significant implications for further studies** on constructing more powerful MLLMs, can **enhance subsequent research endeavors**, and is a **versatile and robust solution** for enhancing multimodal language models. *(Reviewer mp35, Reviewer w8aR, Reviewer V1xr)*

&nbsp;

To address the concerns raised by the reviewers, we have conducted several additional experiments to further demonstrate the superiority of UniKE from various perspectives. And we include these experimental results **in the rebuttal PDF**, which contains 10 tables and 2 figures.

(1) In Tables 1-3, we perform one-step editing and cross-task editing with LLaVA 1.5 as the backbone, also conducting cross-task editing on Blip-2 OPT. The results demonstrate that UniKE is effective across various types of MLLMs.

(2) In Tables 4-5, we compare UniKE with more recent baselines, further demonstrating the proficiency of our proposed UniKE.

(3) In Table 6, we apply UniKE to LLM editing tasks, showing that UniKE can effectively address LLM editing tasks in pure NLP scenarios.

(4) In Tables 8-9, we leverage UniKE on two interesting and challenging tasks with newly constructed data (overediting evaluation and counterfactual editing), finding that UniKE minimizes the influence of overediting and effectively addresses counterfactual editing tasks.

(5) In Table 7, Table 10, and Figures 1-2, we further analyze the editing time efficiency of UniKE and provide additional ablation studies, proving the robustness of UniKE's design.

&nbsp;

These experiments will be integrated into the main body or the appendix of our paper. Next, we will address each reviewer's detailed concerns point by point. We hope to address all of your concerns. Discussions are always welcome. Thank you!

Meta-review: All reviewers acknowledged the novelty of this paper, as well as the solid and thorough experiments conducted. In the initial reviews, there were suggestions to further demonstrate the superiority of the proposed method from various perspectives. After rebuttal phase, the paper received positive feedback from all reviewers. AC has also carefully reviewed the paper, the reviews, and the rebuttal, and concurs with the reviewers' opinions. Therefore, AC recommends acceptance. It would be greatly beneficial to include the additional results from the rebuttal phase in the final version.
Decision: Accept (spotlight)