Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Authors: Zhuoran Zhang
Yongxiang Li
Zijian Kan
Keyuan Cheng
Lijie Hu
Di Wang
Venue: ICML 2025 poster (2025)
Keywords: Factual Recall
Mechanistic Interpretability
Multi-hop Question Answering
Primary area: deep_learning->large_language_models

Abstract: The locate-then-edit paradigm has shown significant promise for knowledge editing (KE) in Large Language Models (LLMs). While previous methods perform well on single-hop fact recall tasks, they consistently struggle with multi-hop factual recall tasks involving newly edited knowledge. In this paper, leveraging tools in mechanistic interpretability, we first identify that in multi-hop tasks, LLMs tend to retrieve knowledge with implicit subject information from deeper MLP layers, unlike single-hop tasks, which rely on shallow layers. This distinction explains the poor performance of current methods in multi-hop queries, as they primarily focus on editing shallow layers with single-hop edit prompts, leaving deeper layers unchanged. To address this, we propose IFMET, a novel locate-then-edit KE approach designed to edit both shallow and deep MLP layers. Beyond single-hop editing prompts, IFMET further incorporates multi-hop editing prompts to locate and modify knowledge across different stages of reasoning. Experimental results demonstrate that IFMET significantly improves performance on multi-hop factual recall tasks, overcoming the limitations of previous locate-then-edit methods.

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Zhuoran Zhang" 12
Yongxiang Li Zijian Kan 23
Keyuan Cheng? 23 Lijie Hu 24 Di Wang 24
Abstract
LLMs (Mazzia et al., 2023). KE methods can be broadly classified into two categories based on whether they al- ter the original model weights: weight-preserving (Zhong et al., 2023) and weight- modiffino approaches (Meng et al., 2022a:b). Weight-preserving methods aim to modify the model's outputs by integrating external memory or leverag- ing strategies such as in-context learning without altering the underlying weights (Cheng al., 2024b;a). Weight- modifying methods can be further categorized into learning- based and optimization-bass weights using gradients but face challenges such as over- fitting and poor generalization. The latter, such as ROME (Meng et al., 2022a) and MEMIT (Meng et al., 2022b), have introduced the "locate-then-edit"" paradigm, which first identifies the knowledge storage layers and then adjusts their weights through optimization techniques to achieve the desired knowledge modification.
The locate-then-eee paradigm has shown signifi- cant promise for knowledge editing (KF) in Large Language Models (LLMs). While previous meth- ods perform well on single-hop fact recall tasks, they consistently struggle with multi-hop factual recall tasks involving newly edited knowledge. In this paper, leveraging tools in mechanistic in- terpretability, first identify that in multi hop tasks, LLMs tend to retrieve knowledge with im- plicit subject information from deeper MLP lay- ers, unlike single-hop tasks, which rely on shallow layers. This distinction explains the poor perfor- mance of current methods in multi-hop queries, as they primarily focus on editing shallow lay- ers with single-hop edit prompts, leaving deeper layers unchanged. To address this, we propose IFMET, novel secate-then-ede KE approach de- signed to edit both shallow and deep MIP layers. Beyond single-hop editing prompts, IFMET fur- ther incorporates multi-hop editing prompts to lo- cate and modify knowledge across different stages of reasoning. Experimental results demonstrate that IFMET significantly improves performance multi-hop factual recall tasks, overcoming the limitations of previous locate-then-edit methods.
methods. The former update
Compared to weight-preserving methods and learning- based weight-modifyin approaches, the locate-then-edi paradigm offers precise editing of the model's internal knowledge with low computational costs (Zhang et al., 2024). However, despite the success of locate-then-edit methods in single-hop fact recall tasks (Li et al., 2024c), they share a common limitation (Zhong et al., 2023): The post-edited model struggles with multi-hop factual re- call tasks involving the newly edited knowledge (see Ta- ble for details). For example, after changing the knowl- edge(fact) "The capital of Spain" from "Madrid" to "Hart- ford", the model correctly answers Q1 "What is the capital city of Spain?". However, when posed with the multi-hop question Q2 "What is the capital city of the country where Pablo Picasso holds citizenship?", it still responds with "Madrid" (Figure (b)). This discrepancy raises a natural question: Has the locate-then-edis paradigm reached its limits for multi-hop factual recall tasks, or does it still hold unexplored potential?
1. Introduction
Large Language Models (LLMs) like ChatGPT (Achiam et al., 2024) and LLaMA-2 (Touvron et al., 2023) have emerged as powerful knowledge bases, demonstrating re- markable abilities in both factual knowledge representation and reasoning over complex queries (Etezadi & Shamsfard, 2022). However, as the need for updating and correcting knowledge within these models grows, research on knowl- edge editing (KE) has gained significant attention, focusing on cost-effective ways to modify specific information in
To address this question, focus the key distinction be- tween the existing locate-then-edit paradigm and the multi- hop fact recall task, which lies in the single-hop editing prompts. Previous methods were primarily designed for single-hop factual recall tasks. They typically associate an edit instance, such as (Spain, Capital, Madrid - Hartford), with a single-hop editing prompt "The capital of Spain is" and use it for the following editing process. Considering
'Peking University 'Provable Responsible AI and Data An- alytics (PRADA) Lab 'South China University of Technology "King Abdullah University of Science and Technology. Cor- respondence to: Lijic Hu <lijic. clijic.hu@kaust.co edu.stted..aa, Di Wang <di. wang&& kau.t.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
(a) Single-hop Prompt Edit
(c) Furtherance Edit with Multi-hop Prompt-
Existing Locate-then-edit
Related fact Carceloon,located,
The country which Barcelona located in has the capital city called Madrid Hartford
Edit:
Edit: The capital of Spain is Madrid
Hartford
Language Model
(Spainn)
(Madrid)
Shallow Layer
Deep Layer
Pablo Picasso
?
?
What is the capital of the country where Pablo Picasso hold a citizenship?
(b) Failed Multi-hop Query Test
Figure 1. (a) The existing bocate-then-edl KE method updates new fact to the shallow layers of the model using a single-hop edit prompt. (b) For multi-hop fact recall tasks, especially when the edited fact in the second or subsequent hops, the hops typically access the deeper layers which outputs the unmodified knowledge. (c) Our method introduces prefix hop for each single-hop edit, creating two-hop edit prompt. We utilize this new prompt to perform furtherance edit, targeting the deeper layers for more effective knowledge updating.
this limitation, propose the following hypotheses: 1) The recall process of the same fact may differ mechanistically between single-hop and multi-hop scenarios; 2) This dis- crepancy leads to the insufficiency of knowledge updates across the model, resulting in unsatisfactory performance on multi-hop factual recall tasks.
implicit multi-hop fact recall mechanisms. Based on these observations, we developed advanced locate-then- edit KE method specifically designed to modify knowledge in both shallow and deep MLP layers, which we named Interpretability-Ce Guided Furtherance Model Editing in a Transformer (IFMET). To surpass the limitations of single- hop prompts, IFMET generates relevant multi-hop editing prompts for each edit instance. To address the issue of in- sufficient knowledge updates caused by differences in the reasoning mechanisms, IFMET extends existing methods by using multi-hop prompts for furtherance editing, effectively addressing cases in the single-hop and multi-hop scenario, as illustrated in Figure 1. Our contributions can be summa- rized as follows !:
-uidded
To test hypothesis 1, we first explored the mechanisms of the pre-edited model when handling multi-hop and single- hop factual recall tasks. Using the example mentioned (Spain, Capital, Madrid), we attempt to illustrate how the model reasons with the implicit subject "Spain" in Q2, com- pared to the explicit mention in Q1- In Scction 3.1, by interpreting the information encoded in each layer's hid- den states using LogitLens (nbstalgebraist, 2020; Dar al., 2023), We find that at the last token position, the information of the implicit subject accumulates before the final answer, which is significantly different from the single-hop scenario.
We first identified key differences in the mechanisms the model uses for reasoning in single-hop versus multi- hop fact recall tasks. In multi-hop scenarios, unlike single-hop cases, the model prioritizes inferring the implicit subject at the last token position, which guides the generation of the final answer.
We then investigate the causal influence of the implicit sub- ject on the final answer and the mechanism by which it affects the prediction. By using causal intervention exper- iments (Li et al., 2024d), our results indicate that in the multi-hop scenario, the implicit subject causally guides the emergence of the final answer by retrieving relevant knowledge from the deeper MLP layers. This contrasts sharply with the single-hop cases (Meng et al., 2022a; 2023), where the subject information is used to retrieve informa- tion from shallow MLP layers. Based on this difference, We provide a more detailed explanation for hypothesis 2: Previous methods leveraging single-hop prompts for edit- ing are insufficient as they only update the relevant knowl- edge in the shallow MI. layers but fail to propagate the changes to deeper layers. As a result, the deeper lay- ers retain unedited knowledge that is only activated by
Next, we pinpointed the components of the implicit subject that influenced the final answer within the deeper MLP layers. We demonstrated that the absence of edited knowledge of these components significantly impacted the model's performance.
We propose IFMET, an advanced Docate-then-ede KE method specifically designed to modify knowledge in both shallow and deep MLP layers using single and multi-hop edit prompts. Experimental results confirm the effectiveness of our method, showing that it suc-
Due to the space limit, we refer readers Appendix A for previous work.
2

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
cessfully overcomes the limitations of previous meth- ods in handling multi-hop factual recall tasks.
probabilities pi over the output vocabulary set V are:
8 = Wo E RIV,
2. Preliminaries
pj softmax (s))
Notations. We define the set of knowledge(fact) as IC- {(s, [,)] S & x R &, where & and R. denote the set of entities and relations respectively. Each tuple (, o) E KC represents that the corresponding entity of subject entity under relation is object entity o. An editing instance can be described in the form of triplet: e = (8, - o²), where of denotes the new edited object in place of the original object o related to s through r.
where We denotes the unembedding matrix used in the final prediction layer of the model. LogitLens also works for the decomposition of hidden states, such as MLPs mi and attention heads of. where hf = hi-1 + mf + af. Log- itLens posits that probabilities and logits provide insights into how the model prioritizes different potential tokens, as indicated by the proportion of related information. So we define Info ( j) as the information related to token j E V contained in hf, positively correlated with s[D] and pi]i]. To account for the probability variations across different layers, we define Info(h;, j) as the layer-wise min-max nor- malized probability (Li et al., 2024d), where L is the total number of layers:
2.1. Factual Recall Tasks
Format of Factual Recall Tasks. Factual recall tasks refer to verifying whether the model M can correctly provide the final answer to a single-hop multi-hop factual recall Q. Q requires multi-step(2 1) reasoning to reach the final answer. Its reasoning process is composed of a chain of knowledge = (1,,71,0) ....... @ (8n; I'n 0n), where 81 the start subject that explicitly given in the question, on is the final answer. There are two different format question prompts for factual recall tasks: Cloze-Formen Format Qtooee and QA- Qua. For instance, given two-hop questions with the knowledge chain like (Paradiso, author, Dante Alighieri) @ (Dante Alighieri, country of citizenship. Italy), Qelose can be "The author of Paradiso is a citizen of",, while Qga is "What country does the author of Paradiso hold citizenship in?". For better clarity, we categorize the multi- hop fact recall into two types: explicit recall step (s1, 01) and implicit recall steps {(22, F2, 02) ..... (8n, Tn, 9.)). }. If the model's s final answer is the same as the answer to the question, the recall is considered successful, which can be represented as M(Qatoze) = on or M(Q = On.
Pineo mnax pili], (1=1,...,1)
Pminlil= = min pibi),
(I=1....))
[nfo(h);:)) =
Pilil-Pinse Gil pi (i) Pmnn
Causal Intervention on Hidden States. Causal interven- tion on hidden states (Li et al., 2024d;a) involves deliber- ately altering specific hidden states a model to observe the resulting changes in various metrics, thereby helping to es- tablish cause-and-effect relationships. This process includes three pivotal components: the intervention operation I to be conducted, the target hidden state or its decomposition 'H selected for intervention, and the effect metric IE which measures the change caused by the intervention I.
Multi-hop Factual Recall under Knowledge Editing. This task assesses whether the post-edited model can effectively leverage the updated knowledge for reasoning in multi-hop fact recall tasks. Given an edit e = (s, r, 0 + o²), the edit prompt Te and a chain of facts C. which includes (s, r, o) as one of its components. The post-edited model must leverage the new factual knowledge (s, r, o²) to answer the multi-hop query. For example, given edit (Paradiso, author, Dante Alighieri Mark Twain), the model's response of "The author of Paradiso is a citizen of" should change from the original answer Italy to the new answer USA.
3. Mechanisms of Knowledge Storage and Reasoning
In this section, we will explore the reasoning mechanisms of the pre-edited model for multi-hop factual recall tasks and how they differ from those in single-hop. Specifically, we focus on two-hop tasks to better illustrate these dis- tinctions, whose knowledge chain is represented as C (81. f1,01) @ (82, P2, 02). In Section 3.1, we primarily inves- tigate the differences in the recall mechanism of the same fact (82, r2, 02) when it severs as the implicit multi-hop step in the chain and the explicit single-hop reasoning step. In Sections 3.2, we explain why the model edited by existing method tends to output the original answer instead of the new edited one in multi-hop $cenario
2.2. Mechanistic Interpretation Tools
LogitLens. LoggtLLens (nostalgebraist, 2020) is a frame- work for interpreting the hidden states (activations) of lan- guage models such as GPT (Brown et al., 2020). For the hidden state hf (token at the I-th layer), the logits sf and
"W employ GPT variants such as GPT-J (Wang & Komat- suzaki, 2021) that position attention in parallel to the MLP, which mathematically equates to models that calculate MI.P sequentially
3

---

tocate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Logit Lens for all 2- cases
cases in the last postion
ing for og? Q3: Which component facilitates the influence of factual recall process from $2 02?
implitit subject
0.8
final
Nomalizzba prot.bllt
Is the information related to $2 accumulated before that of 02 ? We use LogitLenss to examine the accumulation of information related to the implicit subject 82 and the final answer og in the two-hop scenario. The model's predictions for 02, are derived from the last token of the prompt, where crucial information about the resolved implicit subject s2 should be propagated (Biran et al., 2024). Therefore, we focus on the hidden state hı at the 1-th layer of the last token position, analyzing Info(h, $2) and Info(h,, 02) as measures of the information related to $2 and O2 contained in hj.
0.6
94
10 1I 12 15 M 15
17
20 21
23 25 24 25
Layer
(a) Two-hop
Logit Lens for all 3 hop cases in the last postion
subject Final answer
0.8
The results, depicted in Figure 2a, show that Info $2) gradually reaches its peak during middle layers [15-17], while Info(h,, 02) increases and peaks during later layers [21-24]. This pattern suggests that, in multi-hop tasks, the implicit subject 52 is processed during the middle layers before reaching the final answer 02. We conducted a similar experiment by giving 82 explicitly in a single-hop prompt. The results, shown in Figure 2b, indicate that there is no significant peak for the subject information before the final answer probability begins to accumulate, suggesting that in single-hop cases, the accumulation process of the final answer at the last token is not significantly correlated with the subject information.
0.6
04
0.-
21
24
(b) Single-hop
Figure 2. LogitLens results of the last token position at different layers. Yellow line represents the information containing implicit subject $2, i.e., Info(h,,s2)) Blue line represents the information for the final answer, i.e., Info(h,, 02). Larger versions of the sub- figures are available Appendix Figure 6.
Takeaway 1
3.1. How Pre-edited Models Reason Fact Recall Tasks
In multi-hop scenarios, the implicit subject informa- tion consistently accumulates before the final answer at the last token position. However, in single-hop scenarios, since the subject is explicitly given, there is no need for accumulation at the last token position.
For multi-hop fact recall task, the model may employ mul- tiple strategies to answer such tasks, including the formation of a single super-relation (Ju et al., 2024) (81, T'uls on), where Timat = r1 + ... + Tu. or by segmenting the task into one explicit recall step followed by several implicit recall steps to answer step-by-step. Previous research (Hou et al., 2023) suggests that models typically engage in rea- soning by considering each single-hop recall individually.
Does the accumulation of relevant information about $2 causally influence the model's reasoning for og? We propose an intervention experiment where we reduce the information content of at the subject token and last token position, then observe changes in the output probability of the final answer in the last prediction layer.
Based on this, we hypothesize that the model prioritizes deducing the implicit subject $2 and subsequently recalls the final answer 02 based on it. We conduct exploratory experiments about the role of layer hidden state and compo- nents like MLP and Attention head at different positions of prompt. The following sections aim to verify this hypothesis by addressing the three questions: Q1: In the case of multi- hop reasoning, is the information related to 82 accumulated before that of o Q2: Does the accumulation of relevant information about $2 causally influence the model's reason-
Specifically, we replace the hidden state by (in layer & of the last token subject token) with hū, and the corresponding logits st (= Worh)) changes to sf (= Wo of is defined as:
bi). if 82 sr[i],
$
(I)
otherwise,
where minimize the logits corresponding to the tokens in 82 without altering the logits of other tokens, aiming to diminish the effect of s2. Follow the setting in (Meng et al., 2022a), we select the window size = 5, meaning that in each intervention we consider the hidden states across five consecutive layers centered on the targeted layer. This setup
after the attention module, as discussed in (Brown et al., 2020). All experiments in section 3 are conducted using subset of single and two-hop data from MQuAKE- CF (Zhong al., 2023) with the GPT-J (6B) model (Wang& & Komatsuzaki, 2021). More detailed information about the data and the experimental setup is provided Appendix B.2.1.

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
allows us describe the process through causal interven- tion framework, where the target H is hú, the intervention In and the effect / Es are defined as follows:
last layer L. In total, causal intervention is formulated
IA : hi = hy + arg min Wau + Ahr) sil..
Ahe
Im mi my arg min |Wo (my + Am) -s =]? Ami
TE = PLlll P.D..
02,
1Eh = - -Pibl j 02.
where L is the last layer, pr.[j] denotes the original out- put probability of the first token of 02 in the L-th layer, and vf [i] is the probability after the intervention is applied. This approach illustrates how the hidden states and proba- bilities are expected to change when the logits are modified to 8". For computational efficiency, we opt to approximate by using a combination of least squares and minimum-no methods (Lawson & Hanson, 1995) (further details are pro- vided in Appendix C.I).
Figure 3b presents the outcomes of our intervention exper- iments across all layers. The clear positive impact from intervening in the intermediate layers [19-25] is demon- strated in the MLP group, in contrast to negligible effeett observed in the attention head group in the deeper layers. This suggests that the implicit subject 82 at the last token position was used for retrieving the related information 02 from deeper MLP layers. Note that previous work (Meng et al., 2022a; 2023) has mentioned that explicit single-hop tasks primarily rely the subject token position to retrieve information from shallow MLP layers.
Figure 3a presents the outcomes of our intervention exper- iments across all layers, where a brighter color signifies a stronger intervention effect. We found clear positive impact from intervening layers [17-18] ] for the last token group. In comparison, the subject token group demonstrated only slight influences, confined mainly the early layers. This suggests that, in the last token position, the informa- tion of #2 encoded in the intermediate layers plays a crucial role in the probability accumulation process of O2. We at- tribute the significant effects observed in the last two layers to the model's tendency to increase the probability of output articles in these layers, thereby influencing the likelihood of answer generation, rather than involving factual recall mechanisms.
Intervention
last, Joklen, intervent subjecd Intervent
0,10
0,08
Takeaway 2
Unlike the mechanism of reasoning the knowledge in single-hop scenarios, in the reasoning process of the second-hop knowledge in two-hop scenarios, the accumulated implicit subject information has causal effects on the final answer.
22 25
0.00
Infervention Which
(a) Causal intervention results of layer hidden state at the subject token and last token position.
Which component facilitates the influence the factual recall process from $2 to oz? Previous studies claimed that single-hop tasks using subject information to retrieve knowledge from MLP layers (Meng et al., 2022a;b). By comparing the effects of MLP and attention components, we seek to answer how the implicit subject influences the prediction of the final answer 02 in the multi-hop scenario. conducted causal intervention experiments similar to the experiments above but focused specifically on the Attention Head and MLP components. Specifically, we aim to replace my (the input hidden state of the last token in the 1-th MLP Wymm and st = Wom with of is same as in (1), The intervention Im shares the same idea as in (2), except that hy is replaced with my. We follow the definition of the intervention effect Fr. which is the probability change calculated from the
alin in, intervant
-8.66
8.14
8.12
0.10
0.00
mipp in, Intervent
2.00
9.54
0.02
0.50
10 14 Intervention Which Layer
(b) Causal Intervention result of hidden state from Attention head and MLP at last token position.
or attention head) with mi, where we have
Figure 3. Causal Intervention Result: A brighter color significs stronger intervention effect. Note that negative effect values (< 0) are clipped to 0 in both groups for better visualization. (a) is probability change EE, of intervention I,., (b) is probability change 1 F& of intervention In..
5

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Takeaway 3
e Te Cpre Tegee
(Spain, capital, Madrid + Hartford)
The capital city of Spain
In single-hop fact recall, relevant knowledge is re- trieved through the token at the subject position, utilizing shallow MLP layers. In contrast, when the same knowledge serves as an implicit reasoning step in multi-hop, it is retrieved through the token at the last position, utilizing deeper MLP layers.
D (Hartford, mayor, Arulampalam) w
The mayor of the cupital city of Spain is
Gpost
(Burcelona, Counetr Spain) @ The capital city of the country where Barcelona is located
Tcgust
Table I. An example for an single-hop edit instance and its corre- sponding multi-hop prompt.
3.2. Why Existing locate-then-edit KE Methods Failed
Based on the findings above, we can explain the unsatisfac- tory performance of the existing locate-then-edit methods. The factual recall mechanism for the same knowledge dif- fers when serves as a single-hop reasoning step versus an implicit reasoning step in multi-hop reasoning. Conse- quently, for an editing instance (s, 0 - o²) o²), using only the corresponding explicit single-hop prompt for editing in previous methods is insufficient they only update the relevant knowledge in the shallow MLP layers but fail to propagate the changes to deeper layers, which are essential for multi-hop factual recall tasks.
Table 2. Comparison of multi-hop Acc for Drre. Droee.Doott.
Edit Method
QA Format( (6) t
Cloze Format( (%) t
Drec
Drost
Drre
Post
Base PMET
50.62
41.72
20.31
18.63
64.29
2.93
43.37
4.60
4. IFMET
Motivated by our findings on the distinctions between single- hop and multi-hop factual recall processes, we introduce the Interpretability-Guur Guuiddd Guided Furtherance Model Editing in a Transformer (IFMET). IFMET extends the existing locate- then-edit paradigm in two ways: first, constructing multi- hop edit prompts for each edit instance to expand the origi- nal single-hop hop edit prompt, and second, adding furtherance editing step that applies multi-hop edit prompts to deeper MLPs. IFMET thoroughly integrates new knowledge across shallow and deeper MLP layers, significantly improving the model's accuracy and robustness in multi-hop factual recall scenarios.
We provide a conerete example in Table for a better under- standing. Given editing instance e and Te. Existing meth- ods modify the weights of shallow MLPs with Te to make it answer Hartford. Opre and Cpost represent the multi-hop factual recall chains obtained by as an explicit recall step and an implicit recall step, respectively. Tc and Topost represent the corresponding prompts. In this example, the query Top.. should be answered correctly because the ex- plicit fact (Spain, copital, I fart can be recalled in shallow MLPs. However, the Te is still answered with Madrid because the knowledge (Spain, capital, Madrid) stored in deeper MLPs does not change. To verify our claim above, we divide the two-hop factual recall tasks into two sets, Dree and Dosst depending on the position of the edited knowledge within the two-hop reasoning process. Specifically, for an edit instance e (s,r, o")), we have the following two sets:
Multi-hop edit prompt construction. For given edit (s, r, - o²). existing thin methods pro- vide only a single-hop edit prompt such as T. in Table 1. Through Section 3.2, we recognize that the main limitation of these methods lies in ignoring the difference between explicit single-hop and implicit multi-hop reasoning mech- anisms. Single-hop edit prompts only modify knowledge in the shallow MLP layers, leading to poor performance in the Cpost format multi-hop factual recall. Therefore, we aim to construct -ormmtt multi-hop edit prompt for each edit instance, just like mentioned in Table 1. We first transform each edit instance into a two-hop fact recall chain C - (s',r" ,')) + (s, r, o) where o' = s, then transform this two-hop factual recall chain into an edit prompt by using the corresponding prompt templates. The key step is to identify the relevant preceding knowledge (s', s) for e, which can be sourced from any valid knowledge base. Here, we provide two methods: one based on WikiData and the other using the model itself. Further discussion of these two construction methods is detailed in Appendix D.I.
Drre = (s,,r,0") @ (52, T2, 02)}
Dost (s11, 71, 01) @ (s,r,
The specific experimental details are in the appendix C.2. Table 2 presents the results of the comparative experiments. As shown, the multi-hop question accuracy performance of the existing SOTA locate-then then-edit method PMET on Dr is significantly better than on Dros. which aligns with our expectations. This is because the reasoning in the explicit recall step is similar to the single-hop process. After updat- ing the knowledge in the shallow MLP layers by single-hop edit prompt, the newly edited knowledge can be leveraged in Prre- In contrast, for cases in Dpoa. the model fails to produce the correct final answer because existing methods didn't 't update the knowledge in deeper MLP layers.
IFMET, Now we introduce the proposed IFMET frame-
6

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
# Edits
# hops
Editor +CoT
Average
1-edit
2-cdit
3-cdit
4-edit
2-hop
3-hop
4-hop
Base
42.83
36.96
45.27
46.85
48.51
48.9
30.7
48.9
FT
1.9
4.2
0.7
0.3
0.0
3.7
1.4
0.5
MEND
11.5
16.0
11.0
7.3
4.4
13.9
11.3
9.5
ROME
18.1
23.8
20.9
9.0
2.6
33.8
9.1
11.4
MEMIT
12.3
20.5
9.8
5.5
2.6
22.5
6.0
8.4
PMET
17.04
22.63
16.74
11.19
7.84
26.65
12.76
11.7
IFMET (ours)
31.01
30.26
35.21
24.30
31.72
44.06
23.58
25.4
Table 3. Multi-hop accuracy comparison of different methods on the MQUAKE-3K dataset in few-shot setting, showing the Base model's performance on the unedited answer and the edited model's performance on the edited answer.
work, providing a single-hop edit prompt and multi-hop edit prompt for each edit instance. Based on the difference between the single and multi-top reasoning mechanisms we discussed above, in the first edit stage, we the single-hop edit prompt to edit shallow MLPs. In the second stage, we further use the multi-hop edit prompt to edit deeper MLPs.
(s', s) and (s, r, o²) was used, and this prompt was applied to edit deeper layers of the model. Due to space limitations, the flowchart of the algorithm and related implementation details are provided in Algorithm and Appendix D.2.
5. Experiments
Based on previous key-value memories (Geva et al., 2021), our method to edit the MLP is based on the hypothesis that factual knowledge is stored within the Feedforward Neural Networks (FFNs) of MLPs. Specifically, for the 1-th layer FFN, its output of the i-th token's hidden state h is given by: of f(W/"n ht_ W)/ where f()) is the activation function, and h is the input of the th MIP layer (for simplicity, the superscript is omitted in the following discussion). In this context, f(Win/'') functions as the keys, denoted as kú the outputs represent the corresponding values us and Wout denotes the weights of the knowledge stored in the FFN that needs modifying. Such structure is well aligned with the triplet form (s, r, o). where the keys k; correspond to entities of interest s; or some specific fact rz), and values us contain information about o;- Thus, we have Wootk = v for (k, v), which represents the fact (s,r, o) (Geva et al., 2021). We aim to modify Wrout such that Wout k v", where u* contains the information of the new knowledge.
5.1. Experimental Setup
Dataset and Baselines". MQuAKE- 3K (Zhong al., 2023), challenging and widely used dataset designed to evalu- ate models' ability to perform multi-hop fact recall with newly edited knowledge. Each edit instance contains a multi-hop factual recall chain and the corresponding multi- hop textual question. The instance includes at least one fact from the chain to be edited and provides its single-hop edit prompt. Baselines
Base, which refers to the original GPT-J(6BB model without any edits; FT basic fine-tuning method; MEND (Mitchell et al., 2022), which employs meta-learning for weight updating; ROME (Meng et al., 2022a), the classic single-stage bocate-then-edi method; MEMIT (Meng et al., 2023), which extends ROME to edit large set of facts by updating weights in range of layers; PMET, locate-then-eds then-edit method with FFN optimization.
Setup and Hyperparameters. To evaluate the performance of different KE methods, we adopt Multi-hop question an- swering accuracy(Multi-hog Acc) as the primary metric. For each query, the unedited answer denotes the expected old fact before knowledge editing. while the edited answer rep- resents the expected new fact after editing. We use PMET as our primary experimental method for both the first and fur- therance edits and construct multi-hop edit prompts from the knowledge triples of MQuAKE-3K to support our IFMET. Additional details are presented in Appendix E.2.
Motivated by the above, in IFMET, there are two main steps for both the first and second edit stages: Search and Calculate. The Search process identifies the suitable e* through the edit prompt. Then the Calculate process com- putes the change weights Wout using v². These two processes are foundational in existing knowledge editing methodologies. In experiments, we adopt the state-of- oftth-- art locate-then-edh method PMET (Li et al., 2024c). The primary differences between the first and further edit stages are reflected in the used edit prompt and the layers edited. Specifically, for the edit instance e = (s, o + o+), the first edit utilized a single-hop edit prompt To provided by the dataset to edit shallow layers of the model. For the furtherance edit, the two-hop prompt T Top.. composed of
*More details about the dataset Appendix B. We mainly compare IFMET with previous weight-modiffingg approaches, es- pecially these single-stage edit methods applying shallow MLP edits based on single-hop edit prompts. Comparison with weight- preserving methods is discussed in section G.I
7

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Editor
Averaget
Pre f
Post
previous methods by a significant margin. Moreover, the performance improvement of IFMET is consistent across all subsets (e.g.. 2-edit or 2-hop) of the dataset. Notably, in more complex reasoning scenarios, such as when edits>>2 or hops>3, 3, IFMET achieves a performance improvement of two to three times. This demonstrates the adequacy of knowledge updates the IFMET method.
Base
7.70
11.97
6.99
Base+CoT
6.83
8.22
6.60
PMET
11.17
39.20
6,53
PMET+CoT
17.04
43.66
12.63
IFMET
23.04
38.03
20.55
IFMET+CoT
31.01
43.66
28.90
Comparison between the unedited answer and edited an- swer. In Section 3.2, attempted to explain and demon- strate why existing methods still tend to output the unedited answer in multi-hop tasks, especially in the post-type multi- hop fact recall task. Here, we provide a more detailed break- down of the dataset to investigate whether IFMET effec- tively alleviates this issue. Please refer to Appendix E. for the classification of [Pre.Post]. Results in Table show that, in the Post scenarios where the facts are typically treated as implicit reasoning steps, IFMET effectively reduces the output of the unedited answer and improves the accuracy of the correct answer. On the pre-type tasks, as expected, existing methods demonstrate performance comparable to IFMET. This is because they primarily modify knowledge in the shallower layers.
(a) Edited Answer
Editor
Average L
Pre
Post |
Base
39.63
34.04
40.56
Basc+CoT
42.83
41.31
43.08
PMET
29.95
10.09
33.26
PMET+CoT
29.35
12.67
32.13
IFMET
23.08
11.27
25.02
IFMET+CoT
21.32
10.80
23.08
(b) Unedited Answer
Table 4. Multi-hop accuracy comparison for edited and unedited answers using PMET and our editors on the KQ-AKE-3K dataset. Average accuracy calculated as the weighted average of results from these two categories, which have respective quantities of 426 and 2574, Additionally, +CoT denoted the performance incorpo- rating Chain-of-though (CoT) prompt.
Ablation study. With Efficacy metric, which measures whether the model can successfully answer the single-hop fact recall prompt, we comprehensively evaluate the model's s ability to perform both single-hop and multi-hop reasoning using the edited knowledge. The importance of each com- ponent is reflected through comparisons of performance improvements over PMET. From the analysis of the ablation experiments in Table 5, we derive the following conclusions: w/o First: Only modifying the deeper layers using second stage edit with multi-hop edit prompt effectively enhances performance on multi-hop reasoning tasks. However, the absence of the single-hop edit prompt in the first stage re- sulted the shallow MLP layers not being updated, leading to poor performance in single-hop fact recall tasks. This highlights the importance of the two-stage editing process. w/o M ulti: In the second editing stage, we try to use the original single-hop edit prompt instead of multi-hop edit prompt to edit the deeper MLP layers. However, the results corroborate our interpretability analysis which emphasizes the differences between single-hop and multi-hop reasoning mechanisms. Single-hop prompts cannot correctly modify knowledge in deep MLPs, highlighting the critical impor- tance of multi-hop hop prompts. w/o Deeper: In this setup, we try to use the multi-hop edit prompt to edit shallow MLP layers (rather than deeper MLP layers). As observed across the table, there was a consistent minor fluctuation in perfor- mance. In contrast to IFMET's +70% improvement, this underscores the necessity of editing knowledge in the deeper MLP layers when using the multi-hop prompts. More com- prehensive ablation studies and discussions can be found in Appendix F.
5.2. Experimental Results
General performance. Table 3 demonstrates the perfor- mance of various established methods alongside IFMET on MQUAKE-3K. To thoroughly explore the model's ability to leverage the newly edited knowledge, we use Chain-of- Thought (CoT) prompting to guide the model's responses to multi-hop tasks in this experiment. Hops refers to the num- ber of hops of the multi-hop fact chain in the edit instance, with a maximum of 4 hops. # Edits quantifies how many individual facts within the chain are edited, and its maxi- mum value is the same as the maximum number of hops in the instance. What can be observed is that, on the over- all average performance, IFMET consistently outperforms
Editor
Multi-hop f
Efficacy t
IFMET
28.38 (178.0%)
99.56 (112.8%)
Pirst
23.14 (145.1%%
66.59 ((24.6%)
w/o Multi
17.69 (110.9%)
100.00 (1 13.3%)
w/o Deeper
15.07 (15.4%%
99.56 (112.8%)
PMET 15.94
88.21
Table 5. The results of the ablation experiments GPT-J-6B model using a subset of MQuAKE-CF. Both the percentages of decrease(() and increase(f) are calculated relative to PMET as the baseline. The most significant performance decline is highlighted red and the most significant performance increase highlighted in green.
8

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Generalizability. Additionally, we conducted experiments on larger edit batches, newer models and more metrics in Appendix G, all of which showed significant performance improvements, effectively demonstrating the generalizabil- ity of our approach.
Biran, E., Gottesman, D., Yang, S., Geva, M., and Glober- son, A. Hopping too late: Exploring the limitations of large language models on multi-hop queries, 2024. URL https: //tp////axiivii orq wtwp/s/ggg///ooee//.e//eeee/ee/t http//aa///222..222 12775.
Brown, T. B., Mann, B., Ryder. N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss,, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C.,, Chen, M., Sigler, E.,, Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C.,, McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. Language models are few-shot learners. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H. (eds.), Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020. virtual. 2020. URL https: /ttp///eeocc..ss.////ee//e///see neur httpp//ccccppppee/////eeae///
6. Conclusion
We focused on developing locate-then-edit knowledge edit- ing methods for multi-hop factual recall tasks. We first verified that in multi-hop tasks, LLMs tend to retrieve im- plicit subject knowledge from deeper MLP layers, unlike single-hop tasks, which rely on earlier layers. This dis- tinction explains the poor performance of current methods in multi-hop queries, as they primarily focus on editing shallow layers. leaving deeper layers unchanged. We then proposed IFMET, a novel locate-then-edit KE approach designed edit both shallow and deep MLP layers. Ex- perimental results demonstrate that IFMET significantly improves performance multi-hop factual recall tasks.
445700dbbb.00499111111ttllelll Abattract...... html.
Impact Statement
Cheng, K., Ali, M. A., Yang, S., Ling, G., Zhai, Y., Fei, H., Xu, K., Yu, L., Hu, L., and Wang, D. Leveraging logical rules in knowledge editing: A cherry on the top. arXiv preprint arXiv:2405, 15452, 2024a.
This paper presents work whose goal is to advance the field of knowledge editing. We aim to enhance the locate- then-edit paradigm address multi-hop factual recall tasks. There many potential societal consequences of our work, none of which we feel must be specifically highlighted here.
Cheng, K., Lin, G., Fei, H., Zhai, Y., Yu, L., Ali, M. A., Hu, L., and Wang, D. Multi-hop question answering under temporal knowledge editing. ArXiv, abs/2404.00492, 2024b. URL https: / /ppi...... www.steisccnoooe..... org/ / CorpusID:
Acknowledgements
This work supported in part by the funding BAS/I/1689- 01-01, URF/1/4663-01-00 REI/I/5232-01 01-01, and URF/1/5508-01 05000001000--1111111-11-------11 from KAUST, and funding from KAUST- Center of Excellence for Generative AI, under award number 5940.
REI/I/5332-
Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., and Wei, Knowledge neurons in pretrained transform- ers. In Muresan, S., Nakov, P., and Villavicencio. A. (eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers). pp. 8493-8502, Dublin, Ireland, May 2022. Association for Computational Linguistics. URL https: //aclanthology www.:lgg..rrg///2/// aal:22..ccl10 long. 581.
References
Achiam, J., Adler, S., Agarwal, S., Ahmad, I.., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S.,
doi: 10. 10.1663///2002..ellooo.
Anadkat, S., et al. Gpt-4 technical report, 2024. URL https:/ //t:://rxxi wwwxiv.org///o abs abs//303/3333/// 2303 08774.
Dar, G., Geva, M., Gupta, A., and Berant, J. Analyz- ing transformers in embedding space. arXiv preprint ar arXiv:2209.02533. 2022.
Belinkov, Y. Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics, 48(1):207- 219, 2022.
Dar, G., Geva, M., Gupta, A., and Berant, J. Analyzing trans- formers in embedding space. Rogers, A., Boyd-Graber, J., and Okazaki, N. (eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 16124-16170 1707 Toronto, Canada, July 2023. Association for Compu- tational Linguistics. doi: 10. 10.11655/v1/20223.clloom 893. URL https: httsss///claaathhhoaooe wrgpelloooooor/////0 2023. acl-long. 8893.
Belinkov, Y. and Glass, J. Analysis methods in neu- ral language processing: A survey. Transactions of the Association for Computational Linguistics, 7:49-72, 2019.
Graber,
Belrose, N., Furman, Z. Smith, L., Halawi, D., Ostrovsky, I., McKinney, L., Biderman, S., and Steinhardt, J. Eliciting latent predictions from transformers with the tuned lens. arXiv preprint arXiv:2303.088 12, 2023.

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Etezadi, R. and Shamsfard, M. The state of the art in open domain complex question answer- ing: a survey, Applied Intelligence, 53:4124-4144, 2022. URL https: sttp:////.p..ssaa....mmmmmm semant org/CorpusTD:2245 (D:
Hu, C., Cao, P., Chen, Y., Liu, K., and Zhao, J. Wilke: Wise- layer knowledge editor for lifelong knowledge editing. arXiv preprint ExXiv:2422.10985 2024.
Jin, Z., Cao, P., Yuan, H., Chen, Y., Xu, J., Li, H., Jiang, X., Liu, K., and Zhao, J. Cutting off the head ends the conflict: mechanism for interpreting and mitigating knowledge conflicts in language models, 2024. URL https:/ www.wawwvvvo.....oo. abs 22/22442222 18154.
Geva, M., Schuster, R., Berant, J., and Levy, O. Trans- former feed-forward layers are key-value memories. In Moens, M., Huang, X., Specia, L., and Yih, S. W. (eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November. 2021, pp. 5484-5495, Association for Com- putational Linguisties, 2021. doi: 10.18653/V1/2021. EMNIP-MAIN URL https::/p/ / www...og//0..0/ 18653/v1/2022 emm.pmmmmmmppmmmmmm 446.
Ju, T., Chen, Y., Yuan, X., Zhang, Z., Du, W., Zheng, Y., and Liu, G. Investigating multi-hop factual shortcuts in knowledge editing of large language models, 2024. URL https: /tt///axvvvvv////////ee/eeeee org/ www.org///////oo/////..../eeeeee 11900.
Langedijk, A., Mohebbi, H., Sarti, G., Zuidema, W., and Jumelet, J. Decoderlens: Layerwise interpreta- tion of encoder-decoder transformers. arXiv preprint 1xXiv2210.036666 2023.
Geva, M., Caciularu, A., Wang, K., and Goldberg, Y. Trans- former ferd-forward layers build predictions by promot- ing concepts in the vocabulary space. In Goldberg, Y., Kozareva, Z., and Zhang. Y. (eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 30-45, Abu Dhabi, United Arab Emirates, December 2022. Association for Compu- tational Linguistics. doi: 10. 10.18653//1/22022.mmml 3. URL https: htt///acaaaathooooy.... www./rg//022 emnlp-maine
Lawson, C. L. and Hanson, R. J. Solving Least Squares Problems. Society for Industrial and Applied Mathematies, 1995. doi: wwg/doi/////eeeeeooo dob//b///a.
URL https: /eppbss.//pp/bsseeee serppuusssiaa......m 9137/1.978161199
Li, K., Hopkins, A. K., Bau, D., Viégas, F., Pfister, H., and Wattenberg, M. Emergent world representations: Explor- ing a sequence model trained on synthetic task, 2024a. URL https:t/ttaartee wwwrriii....r.oeoooooooooooooooe wwwoooggabbbo 22.00. 13382...
Ghandeharioun, A., Caciularu, A., Pearce, A.. Dixon, L., and Geva, M. Patchscope: unifying framework for inspecting hidden representations of language models. arXiv preprint arXiv:2401 2200::2200.06102, 2024.
Li, K., Patel, O., Viégas, F,, Pfister, H., and Wattenberg, M. Inference-time intervention: Eliciting truthful answers from a language model, 2024b. URL org/ htgg/ab//// /230. 03341.
Gupta, A., Sajnani, D., and Anumanchipalli, G. A unified framework for model editing, 2024. URL https:// arxiv. org/ abs/ / 24022433.2 14236.
Li, X., Li, S., Song, S., Yang, J., Ma, J., and Yu, J. Pmet: Precise model editing in transformer, 2024c. URL https: /tttps:/arxivvorgg org/ /tt.a/b/....00088
Hendel, R., Geva, M., and Globerson, A. In-context learn- ing creates task vectors, 2023. URL httpss://arkt org/ hbs//ab///a 2210 15916.
Li, Z., Jiang G., Xie, H., Song. L., Lian, D., and Wei, Y. Understanding and patching compositional reasoning in 11ms. 2024d. URL https:://aaae org/ /abs/ 2402. 14328.
Hewitt, J., Chen, S., Xie, L. L., Adams, E., Liang, P., and Manning, C. D. Model editing with canonical examples. arXiv preprint 25X1v:2402.06155, 2024.
Liu, Y., Liu, Y., Chen, X., Chen, P.-Y., Zan, D., Kan, M.- Y., and Ho, T.-Y. The devil is in the neurons: Inter- preting and mitigating social biases in language models. The Twelfth International Conference on Learning Representations, 2024. URL https::/ net/forum? www.:mmmddddooooooooooooooo
Hou, Y., Li, J., Fei, Y., Stolfo, A., Zhou, W., Zeng, G., Bosselut, A., and Sachan, M. Towards a mechanis- tic interpretation of multi-step reasoning capabilities of language models. In Bouamor, H., Pino, J., and Bali, K. (eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 4902-4919, Singapore, December 2023. Association for Computational Linguistics. doi: 10. 10.18653/v1/2233 emnlp- 299. URL https:/ / /aclanthology. org/2023. emn semnpp-maing 299..
Mazzia, V., Pedrani, A., Caciolai, A., Rottmann, K., and Bernardi, D. A survey on knowledge edit- ing of neural networks. ArXiv, abs/2310.19704, 2023. URL
org./oorpaaD::24422.5..
10

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Meng, K., Bau, D., Andonian, A., and Belinkov, Y. Locat- ing and editing factual associations in GPT. In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A. (eds.). Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 December 9, 2022, 2022a.
P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, TT, Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Sub- ramanian, R., Tan, X., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, Llama 2: Open founda- tion and fine-tuned chat models. ArXiv, abs/2307 09988, 2023. URL https: / /api. semanticsccooe
Meng, K., Sharma, A. S., Andonian, A. J., Belinkov, Y., and Bau, D. Mass-editing memory in transformer. In The Eleventh International Conference on Learning Representations, 2022b.
Upadhayay, B., Behzadan, V., and Karbasi, A. Cognitive overload attack:prompt injection for long context, 2024. URL https:tp:/// htttt::://axxi..rr/aabb/////// 11222
Meng, K., Sharma, A. S., Andonian, A. J., Belinkov, Y., and Bau, D. Mass-editing memory in a trans- former. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, OpenReview.net, 2023. URL https: //openrevien
Wang. B. and Komatsuzaki, A. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github. com www.ooooooollliooooooooooooooooe kingoflllz/ mesh wesh-transformer jax, May 2021.
Merullo, J., Eickhoff, C., and Pavlick, E. A mechanism for solving relational tasks in transformer language models. 2023.
Wang. Z., Ku, A., Baldridge. J., Griffiths, T., and Kim, Gaussian process probes (gpp) for uncertainty-awann probing. Advances in Neural Information Processing Systems, 36, 2024.
Merullo. J., Eickhoff, C., and Pavlick, E. Language models implement simple word2vec-style vector arithmetic, 2024. URL https::t//// www.i..w.w.waaa....oootaaaa 1613.
Xiao, G., Tian, Y., Chen, B., Han, S., and Lewis, M. Efficient streaming language models with atten- tion sinks, 2024. URL https: http//axxvvvg////e...../.. 2309.
Mitchell, E., Lin, C., Bosselut, A., Finn, C., and Man- ning, C. D. Fast model editing at scale. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenRe- view.net, 2022. URL https: /ttptppp:///peeeeeieeee / forum? d=ODc2xewfOOt.
Yin, X., Jiang, J., Yang. L., and Wan, X. History matters: Temporal knowledge editing in large language model. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38(17), pp. 19413- 2024.
nostalgebraist.
interpreting gpt: the logit
Zhang, N., Yao, Y., Tian, B., Wang, P., Deng, S., Wang, M., Xi, Z., Mao, S., Zhang, J., Ni, Y., Cheng, S., Xu, Z., Xu, X., Gu, J.-C., Jiang, Y.. Xie, P., Huang, F., Liang, L., Zhang, Z., Zhu, X., Zhou, J., and Chen, H. A comprehen- sive study of knowledge editing for large language mod- els, 2024. http:::s: /atxiv.ivvvvv////v org/ hatg//bb///0 2101. 01286.
lens.
https://www lesswrong.
com Com./ppsts//cccBBBaaaaaaoao interppetingg-gpttttee--ee
2020.
Tan, C., Zhang, G., and Fu, J. Massive editing for large language models via meta learning. arXiv preprint axXiv2211.046611, 2023.
Zhong. 7., Wu, 7.,, Manning, C. D., Potts, C., and Chen, D. Mquake: Assessing knowledge editing in language models via multi-hop questions. In Bouamor, H., Pino, J., and Bali, K. (eds.). Proceedings of the 2023 Conference on Empirical Methods Natural Language Processing, EMNLP 2023, Singapore, December 6-10. 2023, pp. 15686-15702 Association for Computational Linguis- tics, 2023. URL https: tttttp://acaaaaiee hwl.atttoooooooooooooooooooolooa org/ 2023 cammm-mmain...
Todd, E. Li, M. L., Sharma, A. S., Mueller, A., Wallace, B. C., and Bau, D. Function vectors in large language models, 2024. URL https: /atriv..//iivve// org/ abs/ 2310. 15213.
Touvron, H., Martin, L., Stone, K. R., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D. M., Blecher, I.., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshom, A. S., Hosseini. S., Hou, R. Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, M., Korenev, A. V., Koura,

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
A. Related Work
Parameter- Editing Knowledge editing refers to modifying outdated, inaccurate, or harmful knowledge in LLMs without the need for retraining. Parameter-editing methods achieve this by adjusting the model's 's internal parameters to update its knowledge while ensuring that information unrelated to the editing domain remains unaffected. An example is ROME (Meng et al., 2022a), which explored the knowledge storage mechanisms in single-hop factual recall tasks based on causal tracing methods and proposed the Rank-One Model Editing method. Together with KN (Dai et al., 2022), it pioneered a paradigm of locate-thee secate-then-ede then-edit, providing guidance for subsequent editing methods. The later extended versions, MEMIT (Meng et al., 2023), MALMEN (Tan et al., 2023), and EMMET (Gupta et al., 2024), further improved ROME by addressing its limitations in lcrge-scale editing, enabling comprehensive edits in a single operation while demonstrating exceptional performance. Meanwhile, PMET (Li et al., 2024c) achieved more precise model editing by decoupling the residual flow of the Transformer into three components: Multi-Head Self-Attention (MHSA), Feed-Forward Networks (FFN), and residual connections, utilizing only the optimized hidden states of the FFN to accurately update FFN weights. Additionally, MEND (Mitchell et al., 2022) trained hypernetwork to efficiently predict LLM weight updates, enabling rapid knowledge editing. METO ( (Yin et al., 2024) optimized the model's 's temporal prediction of facts, editing both historical and new knowledge to reduce forgetting during updates. Wilke (Hu et al., 2024) selected the layers in LLMs that best matched the knowledge pattern for editing, achieving continuous updates and corrections in the model's ' knowledge. Hewitt et al. (2024) used canonical examples to guide the model editing process, enabling fine-tuned adjustments to model behavior. However, these editing methods primarily focus on knowledge updates in specific layers and lack in-depth optimization for knowledge integration and application in multi-hop reasoning, rendering them inadequate for multi-hop questions. In contrast, IFMET enhances model interpretability, guiding more accurate knowledge integration and thereby improving model performance in multi-hop factual recall tasks.
Mechanistic Interpretability LLMs are capable of producing high-quality answers, but their internal workings remain opaque. As a result, the interpretability of LLMs has emerged as both a research hotspot and a critical area of focus. Mechanistic Interpretability refers to the effort to explain the internal mechanisms, decision- maaiion processes, and outputs of LLMs. There are two primary approaches for interpreting large language models (LLMs) in the vocabulary space by examining hidden representations: Probing Classifiers (Belinkov & Glass, 2019; Belinkov, 2022; Wang al., 2024) and Projecting Representations to the Vocabulary Space (Dar et al., 2022; Merullo et al., 2023; Belrose et al., 2023; Langedijk et al., 2023). The former identifies which parts of the model are crucial for specific tasks by training classifiers, known as probes, on hidden representations, while the latter involves mapping intermediate layer representations to the output vocabulary space and analyzing how these projections predict the next word. In this paper, we focus primarily on Projecting Representations. Logit Lens (nostalgebraist, 2020) extracted outputs corresponding to each layer in the decoding space by applying unembedding operations on the intermediate layers of LLMs. Geva al. (2022) analyzed the nature of updates at each layer by comparing differences in logit outputs. Merullo al. (2024) used the Logit Lens to explore how LLMs handle different stages of -antternnn tasks. Dar et al. (2022) mapped attention weights of LLMs to lexical space, showing that these weights encode consistent concepts and relations. Belrose et al. (2023) introduced the Tuned Lens, which improves the capability and reliability of the Logit Lens. Finally, Ghandeharioun et al. (2024) proposed the Patchscopes framework, demonstrating that auxiliary models can represent lexical projections through tuning.
Mechanistic Interpretability serves as a tool for debugging and enhancing L.MS and can be applied to variety of downstream tasks. Xiao et al. (2024) leveraged explanations from multi-head self-attention (MHSA) mechanisms in LLMs by introducing StreamingLLMM, model capable of handling unlimited text without requiring fine-tuning. Through causal tracing, Hendel et al. (2023): Todd et al. (2024) demonstrated that certain attention heads can efficiently encode compact representations of example tasks, leading to improved performance in few-shot prompting. Liu et al. (2024) explored the role of social bias in LLMs, introducing the concept of social bias neurons to explain and mitigate such biases. Furthermore, Li et al. (2024b) proposed an intervention technique during inference, which, based on the interpretability of attention heads, shifts activation values toward "truthful" responses to reduce model hallucinations. In this paper, we analyze the MLP and MHSA components of LLMs to uncover the mechanisms that enable multi-hop -hpp reasoning and, building on our findings, we introduce a targeted knowledge-editing method, IFMET.
12

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
B. Dataset
B.1. Details of MQLAKE-CF-3K
The MQuAKE-3K dataset comprises over 3,000 N-hop questions (where N E {2, 3, 4}), each associated with one or more edits. We use this dataset diagnostic tool evaluate the performance of edited models in integrating newly injected knowledge through editing methods. Two evaluation scenarios can be considered: a) In the first scenario, we perform knowledge editing on single instance d, which may involve up four fact edits. b) In the second scenario, the dataset is divided into groups of k instances (k E {1, 100, 1000, 3000) for MQuAKE-3K. In this case, all instances within a group are processed simultaneously, and the edited facts of these instances are injected into the model at once. This more challenging setting is particularly relevant for editing methods like MEMIT (Meng et al., 2023), which efficiently handle large volumes of edits. The main experiments in the main text focus on the first scenario, while experiments for the second scenario can be found in the appendix G.
B.2. Subset of MQUAKE
B.2.1. 1-HOP AND H-HOP SUBSET FOR MECHANISM EXPLORATION
In exploring the mechanisms of fact recall for one-hop and two-hop queries, this experiment utilized cloze templates as the experimental prompts. We extracted knowledge from MQuAKE that could be answered by GPT-J-6B in a zero-shot setting. This approach ensured that the model could recall the knowledge under the strictest conditions while minimizing the impact of unclear responses on the experimental results. The distribution of various relation types across the two subsets is illustrated in Figure 4.
B.2.2. PRE AND POST SUBSET
To construct the subset, we selected two-hop queries from MQuAKE with Cloze-Format templates and then randomly drew a nearly equal number(>= 300) of cases based on the proportion of relations.
C. Causal Intervention
C.1. Least squares and Minimum-norm method
When performing interventions, need to solve the least squares constraint follows:
arg W Ah)) - s
Ah
In certain situations, the minimum norm method is more effective than directly solving linear systems or using other numerical methods, especially when the system is underdetermined (i.e., there are fewer equations than unknowns) or when there are infinitely many solutions. The minimum method provides solution with the smallest norm among all possible solutions.
To minimize the probability of the intermediate answer j, we replace its logits with the smallest logits of the model's s vocabulary and provide appropriate compensation for the final answer & to maintain the probability of the final answer unchanged. The Ah can be represented as:
Ah Ahg Ahe
- W., [j] Ahg
N [k] - a W [K]
Ahe
TW.D]
The change in the probability of the final answer after causal intervention can be represented by the function f(a): f(a) = P(h", ((h,k) Where f(a) is a monotonically increasing function on the interval (0, 1). We can find the zero of this function using the bisection method, ensuring that the final answer, after the causal intervention, remains within an acceptable error margin with unchanged probability.
13

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Relation Counts for l-hop
[643
1600
1400
1200
Count
1000
800
600
437
400
200
205
164
126
101
89
88
68
66
56
50
20
16
0
P641
P27
P178
P449
P413
P140
P159
P37
P19
P176
P495
P30
P20
P36
P937
P50
P740
P108
P407
P170
P136
P175
P69
P26
Relation
Relation Counts for 2-hop
229
210
200
181
Count
100
70
42
40
35
26
18
17
12
P27
P641
P413
P30
P36
137
P495
P937
P50
P19
P159
P20
P286
P26
P69
P1308
P108
P170
P488
P112
P176
P740
P136
P6
P140
Relation
Figure 4, Relation number for 1- 1-hop and 2-hop
14

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
C.2. Comparative experiments on the same fact at different positions in multi-hop factual recall
We sampled two subsets with approximately equal sizes from the MQuAKE-3K dataset, detailed in Appendix B.2.2. By applying the SOTA bocate-then-cee method PMET to layer [3-8], which follows (Li et al., 2024c), present the percentage of cases where both pre-edited and post-edited models answer successfully in QA format or Cloze format under different edit batches. Notably we show the performance of pre-edited model on unedited answer, well as the edited answer for the posted-edited model.
D. Details of IFMET
D.1. Detailed Multi-hop Edit Prompts Construction Process
We provide two methods for constructing multi-hop edit prompts. The first method is used in the main experiments of this paper, while the performance of multi-hop edit prompts generated by the second method is discussed in the appendix G.
WikiData as Knowledge Base Practically, we utilize WikiData' to construct. We start by extracting all 2615 subjects from the MQUAKE dataset's edits and deduplicating them to form set of subjects S, = {silé = 1,...), }. For each subject We then perform a WikiData SPARQL query to identify a set of triplets for each subject sy: Sup = {(s', r', o') = 82). The query illustrated in Table 14. To keep the query complexity within an acceptable range, we collected all relationships that have appeared MQuAKE and restricted r' to those that have occurred in the relation set. To ensure the reliability of these facts, we then use the prompt 1 to filter out the answerable (s', r', s) triples. For each edit case (s, r; 0 o*), we able to construct two-hop edit template (s's') with the multi-hop chain C' = rs',r's) @ (s, 0*).
Model as Knowledge Base We used simple prompt in table 13 to retrieve relevant knowledge directly from the model to be edited for constructing the multi-hop edit prompt. Due to computational and time constraints, we limited each case to a minimum of one multi-hop edit prompt and a maximum of five multi-hop edit prompts.
D.2. Detailed Edit Process
Algorithm 1: IFMET
Data: Requested edits [(s,, - of) Supplementary set Sup = ((5, r4 &:)) model M, first
layers 4, furtherance edit layers la
Result: Modified model MM containing edits from
for (8,,7s,07 o7) E E do
// First Edit Process
:
Generate the single edit prompt Tr, (8x)
Optimize of + (earch(Tr,
// of for every new fact
.end
for
E 1 do A + Calculate((oj...... (p. xx]) :
Update weights of Shallow MI.Ps Compute weight change with target vectors Update layer MLP weights model
6
W W + A
end
Construct the multi-hop Chain C (sf,r),.)) (8.,).0); Furtherance Edit Process
(o..r.))) E Sup
"
Generate the multi-hop edit prompt T
Optimize +- Search(To(s())
13 end
for
do
Update weights of Deeper ML.Ps
A +- Cateulate((et..... +
W + W A
16
end
Our method primarily consists of a first edit (step 1-8 in Algorithm 1) and a furtherance edit (step 9-17 in Algorithm 1).
www.wikidata.oorg
htapps//querr.wiiddaattrom
15

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Each single edit process obtains target weights via optimizing the objective of knowledge preservation and editing:
arg min
A JlWKK Woot
[WKE VEll
Preserve
Edit
where Ko = 1 RB1 ... ] and V/- = Wout Ko contain all the knowledge we want to preserve, KE [kg k2 ... k is the matrix containing the edits we try to make and V. = [V I... & represents the target representations of the new knowledge. (Kg, VE) corresponds to the edited fact set {(*,, Tú, of i 1,2. F.). We consider the target weight W as the sum of the original weight Wout and the incremental weight A, as explicated in (Li et al., 2024c), a closed-form solution to the incremental weight can be derived:
A = RC + KgKE)-1 R (VE- - Wool Wow"K)) Co a KoKY.
(3)
Thus, solving the optimal parameter W is transformed into calculating edited fact representation {(ks, v2) 1.... E}. In this process, an edit instance = (s, r, o - o*), (Rie, te) the pre-edited fact (s, r, o) and (kee denotes post-edited (s, r, 0*). To obtain the target representations of the new knowledge 2. ve + 6, we optimize the learnable parameter vector 8 to modify the original value vector. Search is the process of obtain the optimized o through gradient descent:
P
8 =arg min L(0) - DDKI. (PM. I1' [T] 11' T)) Pp
Σ
log IP M. [o") prefy + To]
where 7' is the KL prompt, such as "s is a" and 5' is the tokens excluding the token for the answer o", Te is the prompt for editing, such as "The capital of Spain is", and serve as the scaling factor for adjusting the loss. Calculate process is using the ve to slove the A which a function of e.. involves substituting the values Vg = [ue |... 122 corresponding series of edits into (3) to compute the A.
The primary differences between the first and furtherance edits are reflected in the edit prompt T. and the layers edited. For example, for the edit instance = (s, o*), the first edit utilized single-hop edit template T. Tr(s) provided by MQuAKE to edit layers [3.8] of the GPT-J model in the subject last token position. For the furtherance edit, a two-hop prompt T. = (s')') composed of a support case (s',r,)) s) and (s,),o"), and this two-hop edit prompt was applied to edit layers [16 of the GPT-J model in the last token position.
E. Addition Experimental Settings
E.1. Criteria for classifying dataset into pre and post.
Consider multi-hop question composed of triples. We define the positions of edits (with the index starting from 1) the set (ei, . Cm) J. where m represents the total number of edits. The important insight is to divide according to whether only the first hop is edited. Edits only occurring in the first hop are classified as pre, while those hold edits more than one are labeled post.
E.2. Experimental Settings
When constructing the multi-hop edit prompts, for each edit case, no more than three triplets per relation were added. The relation types of the multi-hop edit prompts set are the same as MQUAKE. We set the edit batch sizes to 1.
In both the first and furtherance edits, our configuration for PMET adheres to the settings specified by (Li et al., 2024c). Initially, we set y and 0 < μ S to manage the retention of the model's 's original knowledge. As μ increases, the retention level also increases, while o exhibits the opposite trend. After maximizing the probability of the target knowledge. we reduce &p 0.1 to preserve the original knowledge much as possible. Optimization is halted when DRL < 0.01. On GPT-J, for estimating the covariance matrix (i.e., the set of previously memorized keys Co), we sample 10,0000 times on Wikitext in fp32 precision and set À = 6000. When optimizing, we limit the total optimization steps to 30 with a learning rate 0.2. All our experiments were conducted using the MQuAKE dataset. To test the accuracy of answers to multi-hop questions, we adhered to the few-shot in Table 12 and Chain of Thought (CoT) templates in Table 10 and procedures as outlined in (Zhong et al., 2023).
16

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
F. Ablation Study
Our mechanism analysis has identified that the existing editing methods with only single-hop edit prompts fail to adequately modify knowledge in the deeper MLP layers, resulting in poor performance on multi-hop factual recall tasks. Additionally, our findings suggest that implicit multi-hop step dependencies rely on the knowledge provided by these deeper MLP layers. Based on these interpretability results related to the mechanistic difference, we propose the IFMET. Given the distinctions between IFMET and other existing methods, we highlight four key components, especially in the furtherance edit: two-stage modification, the use of multi-hop edit prompt, editing the last token position, and updating knowledge in the deeper-layer MLPs during the second stage, as illustrated in the table 6.
To investigate whether the IFMET method effectively balances the requirements of general knowledge editing and multi-hop fact recall tasks, we constructed the paraphrase set and neighborhood set for subset of the MQuAKE-3K dataset, , following the approach used in the COUNTERFACT dataset (Meng et al., 2022b). We conducted experiments under two configurations: edit batch 1 and edit batch 100 and focused on analyzing the roles of these key components the following metries:
Efficacy measures whether an edit has been successfully applied to model and whether the model can successfully answer the single-hop fact recall prompt. It is calculated as the percentage of edits where P(edited answer) > P(unedited answer) for a given query prompt used during model editing.
Multi-hop measures whether the multi-hop question related to the edited knowledge can be successfully answered by the model.
Paraphrase evaluates the model's generalization ability under edit. It is defined as the percentage of edits where P(edited answer) > P(unedited answer) for paraphrases of the query prompt.
Specificity assesses the locality of the model editing, i.c., whether the edit of a specific fact affects other facts stored within the model. Specificity score is defined as the percentage of facts in the neighborhood of the edited fact that remain unchanged after the edit.
In Table 7, we have utilized the PMET as a baseline to assess method performance. The importance of each component is reflected through comparisons of performance improvements over PMET. PMET's performance exemplifies a single-stage locate-then-ede approach editing shallow MLPs based on single-hop prompt. From the analysis of the ablation experiments, we derive the following conclusions:
IFMET: Firstly, it can be observed that the implementation of IFMET achieves the best performance in Multi-hop Acc. In the second stage of editing, we employ multi-hop edit prompts alongside deep editing techniques. Across all the experimental tables mentioned, IFMET consistently demonstrates a substantial improvement in inferential performance compared to PMET, As the edit batch size increases, IFMET continues to sustain performance improvements. However, it is worth noting that the IFMET framework also leads slight decrease in the specificity metric, indicating that more extensive modifications to the model's knowledge can somewhat compromise specificity. But, as mentioned in previous works, the pursuit of balanced performance across the four metrics Multi-hop Efficacy, Specificity, Paraphrase-we believe IFMET performs exceptionally well in this regard as well.
w/o First: When only applying the second edit stage in IFMET, which modifies the deeper layers using a multi-hop hop edit prompt, it effectively enhances performance on multi-hop reasoning tasks. However, the absence of first-stage editing results in unchanged knowledge in the earlier layers, leading to poor performance in single-hop fact recall tasks. This highlights the importance of the two stages, where each stage is indispensable.
Method
Stage
Edit Prompt
Position
Layers
Previous
Only one
Single-hop
Subject Last Token
Shallow
First Furtherance
Single-hop Multi-hop
Subject Last Token
Shallow
IFMET
Last Token
Deeper
Table 6. The main difference between IFMET and previous methods. The term Stage refers to the phases of the editing process, Data denotes the query utilized for editing, Position specifies the token position where the editing is applicd, and Layers indicate the edited layers.
17

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Edits
Editor
Multi-hop
Efficacy
Specificity
Paraphrase
IFMET
28.38 (178.0%)
99.56 (+12.8%
69.87 (111.11)
90.17 (15.3%)
w/o First
23.14 (145.1%)
66.59 (124.6%%
62.61 (120.3%%
41.48 (151.6%)
w/o Multi
17.69 (0.9%)
100.00 (113.3%)
77.71 (11.2%)
86.24 (f0.7%)
w/o Last
18.12 (1.66%
88.21 (10.0%)
78.60 (10.0%)
86.24 (10.7%)
w/o Deeper
15.07 (15.4%)
99.56 (2228%)
70.31 (1.0.6%)
86.90 (+1.5%
PMET 15.94
88.21
78.60
85.59
IFMET
27.07 (164.8%)
96.29 (18.1%)
69.89 (19.4%)
84.28 (13,8%)
w/o First
22.71 (135.1%)
73.36 (117.8%)
69.21 ((10.2%)
34.72 (157.3%)
w/o Multi
17.25 (12.6%)
99.13 (111)3%
76.63 (70.6%)
84.06 (13.5%)
100
w/o Last
15.94 (,
89.08 (10.0%)
76.85 (+0.3%)
81.55 (+0.4%)
w/o Deeper
16.16 (13.9%)
99.56 (1118%)
74.67 (13.2%)
81.00 (1.0.%%
PMET 16.81
89.08
77.07
81.22
Table The results of ablation experiments on GPT-J-6B model using a subset of MQuAKE-3K, Both the percentages (ecrease(L) and increase(t) calculated relative to PMET the bascline. The most significant performance decline is highlighted in red and the most significant performance increase is highlighted in green.
w/o Last demonstrated the importance of editing the last token position in the second stage. This is consistent with the findings from the mechanism exploration in Section 3.1, where subject information converges at the last token position and has a causal effect on the retrieval of the knowledge about the final answer.
w/o Multi: This represents that, in the second editing stage, we continued to use the original single-hop edit prompt instead of the multi-hop edit prompt to edit the deeper MLP layers. The performance improvement in efficiency somewhat indicates that single-layer edit prompts can edit knowledge in deep MIP for single-hop scenarios, but there is significant improvement in multi-hop performance. This suggests that, due to the difference in mechanisms, the knowledge used in implicit reasoning steps that have not been sufficiently modified in the deep MLP layers remains unedited. It also indicates that using single-hop prompts combined with deep MLP editing is ineffective, highlighting the critical importance of the multi-hop prompts.
w/o Deeper: In this setup, the second-stage editing was modified use the multi-hop edit prompt combined with shallow MLP editing(rather than deeper MLP layers). If this also shows a significant performance improvement in multi-hop accuracy, it would indicate that merely expanding with the multi-hop edit prompt, without considering its mechanism the deeper MLP layers, can enhance results. However, observed across the table, there was consistent minor fluctuation in performance (ranging from -5.4% to +3.9%). In contrast to IFMET's own +70% improvement, this underscores the importance of editing the deeper MLP layers when using the multi-hop edit prompts set.
In light of the results from the ablation experiments w/o Multi and w/o Deeper, we emphasize that it essential to apply multi-hop edit prompts the deeper MLP layers for knowledge editing, ensuring mechanism match, in order to effectively improve the performance of multi-hop factual recall tasks. In conclusion, we can summarize that the four components proposed by IFMET are essential for performance improvement, also highlighting the importance and validity of our interpretability exploration work.
G. Generalizability of IFMET
In this subsection, We explore the generalizability of our method from four key perspectives:
Generalization to other models. We repeated the ablation experiments LLaMA-2-7B to evaluate the generalizability of IFMET. The results shown in Table 8 are consistent with those observed on GPT-J, highlighting the importance of the four components in IFMET as well as the superiority of the method itself on I.L.MA-2-7B model. Considering both the interpretability analysis and experimental outcomes, we conclude that our analysis and method are equally applicable to larger models, such as LLaMA-2.
Generalization other edit batch number. In addition to editing a single instance at a time, we also tested scenarios
18

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Edits
Editor
Multi-hop
Efficacy
Specificity
Paraphrase
IFMET
28.38 (173.3%)
99.78 (113.7%)
65.50 (410.8%)
75.00 (123.1 %)
w/o First
25.76 (157.3%)
56.55 (1.5..6%)
62.18 (1115.%)
39.08 (4.35.9%)
w/o Multi
19.43 (118.6%)
98.68 (112.4%)
70.35 (14.2%%
69.00 (t13.2%)
w/o Last
15.72 (14.1%)
88.86 (11.2%)
73.03 (10.6%)
61.90 (t1.6%)
w/o Deeper
15.28 (16.8%)
96.72 (110.1%)
65.61 (410.7%)
66.99 (19.9%)
M Multimodel
26.86 (164.0%)
96.07 (19.4%)
63.32 (413.8%)
74.24 (121.8%)
PMET
16.38
87.77
73.41
60.92
IFMET
27.29 (176.0%)
97.82 (14.2%)
65.50 (19.9%)
84.17 (t14.2%)
w/o First
24.67 (159.2%)
64.41 (131.4%)
63.97 (412.0%)
41.81 (143.3%)
w/o M uutti
15.07 (42.8%)
99.34 (15.8%)
71.83 (41.2%)
76.64 (14.0%)
100
w/o Last
13.97 (19.9%)
94.32 (10.4%)
72.14 (40.8%)
74.67 (1133%)
w/o Deeper
15.94 (12.8%)
96.51 (12.8%)
69.48 (44.4%)
75.44 (12.3%)
w M modell
22.49 (145.1%)
96.07 (t2.3%)
63.32 (112.9%)
79.26 (17.5%)
PMET
15.50
93.89
72.66
73.69
Table The results of the ablation experiments LLaMA-2-7B model using subset of MQuAKE-3K. Multimoded represents the multi-hop edit prompts generated by model itself to modify deeper MLPs. Both the percentages of decrease(( and increase(t) are calculated relative IFMET the baseline.
where 1,000 and 3,000 instances are edited together. The results, shown in Figure 5, demonstrate that IFMET outperforms existing methods across all edit batch sizes.
Construction of the multi-hop prompts. In IFMET, we emphasize the importance of constructing a multi-hop edit prompt for each edit instance. It is important note that any other valid knowledge base replace WikiData and SPARQL. A straightforward alternative is to treat the model itself reliable knowledge base for extracting relevant knowledge.
The results of substituting the multi-hop edit prompt with one generated by LLaMA-2 itself for editing are also shown in Table 8 called w Mullimadel- The results show a significant improvement over the one-stage PMET, with performance trends aligning closely with those IFMET. Notably, minimal effort was invested in designing the knowledge retrieval prompt, and no additional filtering or preprocessing was applied. This suggests that the multi-hop edit prompts generated by the model represent a relatively low-quality version, effectively serving lower bound for the method's performance across various metries. Despite this, it still outperforms existing one-stage methods. This highlights the inherent superiority of the IFMET framework and demonstrates the feasibility of using the model itself to construct the prompts set.
Time complexity of IFMET. We compared the time complexity of IFMET with that of the one-stage PMET method it
Model
Method
Time
MEMIT
4.5s
GPT-J-6B
PMET
5.0s
IFMET
9.7s
MEMIT
2.1s
LLaMA-2-7B
PMET
2.0s
IFMET
3.4s
Table 9. The average time required to edit single case varies across methods, For the two one-stage methods, MEMIT and PMET, this corresponds to the process optimizing the shallow-layer MLPs using single-hop queries. For IFMET, the process includes updating the deeper-layer MLPs using two-hop edit prompts.
19

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
builds upon, the result is shown in Table 9. On average, the time required to perform complete edit for single on GPT-J using IFMET was approximately 2.5x that of PMET. For LLaMA-22, the time required was about 1.5x that of PMET. We believe this is within an acceptable range, and as the editing speed of the single-stage method improves, the IFMET framework will correspondingly become faster.
G.1. Comparison with
Methods
Although there have been some weight-preserving editing methods(c.g. RAG-based Methods) accessing good performance for multi-hop question answering in the KE scenario, we believe that exploring the locate-then-edie methodology remains meaningful for several reasons:
1. From the perspective of understanding internal knowledge utilization: The mechanisms underlying model's use of internal knowledge differ fundamentally from those governing the use of external knowledge (Jin et al., 2024). Investigating the potential of locate-thent then-edtteeddit methods holds significant value for advancing the interpretability of internal knowledge processes, laying the groundwork for deeper insights and practical implementations. Additionally, believe this approach enables more fundamental and precise modification of knowledge.
2. From a practical standpoint: Methods based on aerreval-auggenteed generation (RAG) require providing extensive contextual input tokens, posing substantial challenges in terms of computational efficiency and hardware demands. And these methods face several challenges. Instead of injecting knowledge into L.MMs, they retrieve related facts stored in memory for editing. As result, their retrieval success rates become crucial, particularly when managing complex real-world scenarios involving exponential growth in knowledge updates. Moreover, we argue that an over-reliance modifying knowledge through external contexts introduces security risks, as it may be exploited for data theft and attacks (Upadhayay et al., 2024), especially in real-world applications.
H. Additional Experimental Results
(a) MQUAKE-3R 3K
40
ROME
MeLLo
35
MEND
(FUSS Base
MEMIT
30
Multi-hop Acc. (%)
PMET
25
20
15
10
5
0
0
500
1000
1500
2000
2500
3000
Number of Edited Batches
Figure Multi-hop Performance Comparison of different methods across batch sizes on MQUAKE-3K. Base this table represents unmodified GPT-J-6B model, and report its performance unedited answer.
20

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
Logit Lens for all 2-hop cases in the last postion
implicit subject
0.8
final answer
Normalization probability
0.6
0.4
0.2
0.0
12 13 14 15 16
18 19
20
21
22
23
24
25
26
27
Layer
(a) Two-hop
Logit Lens for all 1-hop cases in the last postion
subject
0.8
final answer
Normalization probability
0.6
0.4
0.2
0.0
6
11
12
14 Layer
13
15
18
19
20
22
24 25 26
27
(b) Single-hop
Figure 6. LogitLens results the last token position at different layers. (a) Yellow line represents the information containing implicit subject 82, i.c., Info(h;, 82). Blue line represents the information for the final answer, i.c., Info(h;, 02). (b) Yellow line represents the information of subject ie.. Info(he, s), and Blue line represents the information of answer ie.. Info(he, o). Larger versions of the sub-figures are available in the Appendix.
21

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
I. Prompts And Templates
Question:
What is the capital of the country where Plainfield Town Hall is located?
Thoughts: Plainfield Town Hall is located in the country of the United States of America. The capital of United States is Washington, D.C. Answer: Washington, D.C.
Question: In which country is the company that created Nissan 200SX located?
Thoughts: Nissan 200SX was created by Nissan. Nissan is located in the country of Japan. Answer: Japan
[3 in-centext demonstrations abbrevisted]
Question: Who has ownership of the developer of the Chevrelet Corvette (C4) Thoughts:
The developer of Chevrolet Corvette (C4) is Chevrolet. Chevrolet is owned by General Motors.
Answer: Model Generated Answer Goes Here
Table 10. The template of the prompt we used for asking multi-hop questions using chain-of-thoughts. Our CoT setting to cut the prompt Thoughts: so that LM to generate thought itself (pressured by the prompt and ICL examples). the same multi-hop problem, we generate cot and its corresponding answers three times to ensure that possible errors are avoided.
Lin-oonext-leerniie examples:
Q: Who is the develeper of Telegram? A: Telegram FZ-LIC
Q: Who
develeper of Microsoft Windows? Microsoft
Q: Who is the developer of PlayStation 2? A: Sony Interactive Eatertainment
Q: Who is the developer of iTunes? A: Apple Inc.
Q: Who is the developer of SR-71 Blackbird? A: Kelly Johnson
Q: Who the developer of Moblin?
Linux Foundation
Q: Who the developer of Xbox 360? A: Microsoft
Q: Who is the developer of Kinsey scale? A: Alfred Kinsey
(Query during inference)
Q: who is the developer of Steamos? A:Valve Corporation
Table 11. An example of the prompt used to recall single-hop fact
22

---

Locate-then-edit for Multi-hop Factual Recall under Knowledge Editing
(In context" context-leacning examples:
Q: what is the country where The Rotunda is located? A: United States of America
2: which country was Tchar Butbul granted citizenship? Israel Nissan 200SX created by? Nissan the country where Prickly Pear grows located in? Europe Q: what is the capital of the country where Plainfield Town Hall is located? A: Washington, D.C.
Q: who
Q: what continent
Q: In which country is the company that created Nissan 200SX located? Japan
Q: Who was Dodge Ram SRI-10 created by? Dodge
Q: who the spouse of Joe Biden? A: Jill Biden
Q: which continent is the country where the director of "My House Husband: Ikaw Na!" was educated located in? A: Asia
Q: What country
the location of the Battle of Pressburg? A: Hungary
Q: Who the spouse of the US president? A: Jill Biden
Q: Who has ownership of the developer of the Chevrolet Corvette (C4) A: General Motors
Q: who is Joe Biden married to? A: Jill Biden
Q: what is the country of citizenship of Charles of Spain? Spain
Q: who was Chevrolet Biscayne created by? A: Chevrolet
Q: what is the name of the current head of state United Kingdom? A: Elizabeth II
Q: multi -hop question
Table 12. The template of the prompt we used for asking multi-ho questions using few shot.
(In-context -learning examplest
Input: The country that has nationals <mask> is Located in the continent of Asia
Output: Hitomi Yaida
Input:
The country that has nationals <mask> has the official language of Italian
Output:
Giorgio Chiellini
Input:
The university where <mask> was educated located its headquarters in the city of
Vienna
Output:
Michael Haneke
Input:
The country that has nationals <mask>, its capital is Washington
Output:
Lou Pearlman
Input:
The person who found <mask> is a citizen of United states of America
Outout: Microsoft
Input: The creator of <mask> hails from Italy
Output:
Ferrari
Input:
The author of <mask> is a citize zen of United States of America
Output:
Holly Potter
Input:
The person who discovered <mask> lives in Germany
Output:
Volkswagen
Input:
question
Table template the prompt we used asking LLaMA-2-7B to generate multi-hop edit prompt
SELECT ?subject jeubjectLabel ?predicate ?predicateLabel WHERE ?subject ject Ppredicate wd:ss.
FILTER (?predicate IN (wdt:relation) SERVICE wikibase:label ba:serviceParan wikibase: language "en". LIMIT 50
Table 14. The template of the SPARQL Query we used for prefix fact recall triplets for specific subject.
23


Reviews:
[{"review_id": "cbozkNz96S", "reviewer": "Reviewer_iMRZ", "summary": "This paper identifies significant limitations in the existing knowledge editing methods based on the \"locate-then-edit\" paradigm, particularly when applied to multi-hop factual recall tasks. To explore these limitations, the authors first employed the LogitLens technique and discovered that multi-hop queries tend to cluster implicit subject information at the last token position, while single-hop queries do not. Next, they used causal inference techniques to confirm that implicit subjects impact the final inference results and pinpointed that the key component involved in this process is the deep MLP layer. Based on these findings, the authors analyzed existing knowledge editing methods and found that most of them overlook the editing of deep MLP layers, focusing primarily on shallow MLP layers, beacuse of the use of single-hop edit prompt. This oversight leads to limitations in the performance of multi-hop factual recall tasks. To address this issue, the authors propose IFMET, a method that balances the editing of both shallow and deep MLP layers with multi-hop edit pormpts, thereby enhancing the capability of the \"locate-then-edit\" paradigm for multi-hop tasks.\n\n## update after rebuttal\n\nMy concerns are addressed, and I maintain my positive score.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your recognition of our work. Here is our response.\n> Response to Q1:How is the dimension of K0 determined in Equation 3?\n\nK0 represents the knowledge we try to preserve when modifying specific fact. Throughout the calculation process, we do not calculate K0 separately, but follow the practice in Rome[1], treating $C = KK^T$ in Equation 3 as a constant that we pre-cache by estimating the uncentered covariance of k from a sample of Wikipedia text. Our second moment statistics C ∝ E[$kk^T$] are computed using 100,000 samples of hidden states k computed from tokens sampled from all Wikipedia text in-context.\n\n> Response to Q2: Is the performance of the methods in Table 3 related to the number of edits? As shown in the table, the performance of 3-edit is worse than both 2-edit and 4-edit.\n\nWe believe that the performance of answering multi-hop questions after the model implements knowledge editing is usually inversely proportional to the number of edits. However, in the actual implementation of the KE method, it is also related to the quality of the prompt used for editing. We checked the prompts used in the 3-edit part and found that the confidence of the model when answering based on them was relatively low, which means that the model is more likely to be biased when recalling, which means that the multi-hop prompt in 3-edit cases may not be optimal, but we believe that constructing a better multi-hop prompt can be left for future exploration. This article only represents the lower limit performance of the proposed framework.\n\n[1] Locating and Editing Factual Associations in GPT", "claims_and_evidence": "The claims made in the submission are supported by clear and convincing evidence", "methods_and_evaluation_criteria": "Yes, The proposed method addresses the issue where post-edited models struggle with multi-hop factual recall tasks, particularly those that involve newly edited knowledge. Through extensive comparative experiments and ablation studies across various models, the paper demonstrates the effectiveness of the proposed approach.", "theoretical_claims": "Yes, the paper provides a closed-form solution to the incremental weight, and this section includes theoretical claims. The solution process is correct and free from issues.", "experimental_designs_or_analyses": "Yes, based on the content of the paper, the validity and soundness of the experimental design and analysis are primarily demonstrated through comparative experiments across multiple models and ablation studies. The experimental design addresses several key aspects: it identifies the key differences in the mechanisms the model uses for reasoning in single-hop versus multi-hop fact recall tasks. The paper evaluates multi-hop tasks across various models and conducts a well-designed set of ablation experiments to demonstrate the effectiveness of the proposed method. The design and analysis methods are appropriate and effective, with no obvious issues or flaws found. All experimental and analytical steps have been thoroughly validated.", "relation_to_broader_scientific_literature": "Knowledge editing is an active research area in natural language processing. IFMET advances this field by introducing a two-stage editing strategy (shallow and deep editing), especially addressing the challenges in multi-hop reasoning tasks. IFMET also modifies knowledge based on interpretability guidance, which aligns with recent research on model interpretability and reasoning transparency.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "**Strengths**\n- Empirical analysis: The paper provides a solid empirical analysis, validating two hypotheses about how large language models (LLMs) process multi-hop queries compared to single-hop ones. Using interpretability tools like Logitlens and causal intervention, the authors identify key insights that enhance our understanding of LLM reasoning and model interpretability.\n- Effective approach: The IFMET solution is well-founded and effectively addresses multi-hop reasoning challenges, showing notable improvements in performance, with potential real-world applications.\n- Thorough experimentation: The experimentation is thorough, with ablation studies and comparisons to existing methods, which effectively demonstrate the approach's superiority in multi-hop factual recall tasks.\n\n**Weaknesses**\n- The paper's formatting needs improvement, and the content in the experimental section is relatively limited. Some important results, such as the ablation experiments on LLaMA2, are presented in the appendix. It is hoped that adjustments can be made in the camera-ready version.\n- I strongly recommend that the authors include a discussion of in-context editing approaches in relevant sections, such as Related Work or Appendix.", "other_comments_or_suggestions": "See weaknesses", "questions_for_authors": "1.\tHow is the dimension of K0 determined in Equation 3?\n2.\tIs the performance of the methods in Table 3 related to the number of edits? As shown in the table, the performance of 3-edit is worse than both 2-edit and 4-edit.", "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": "SJNeAbFGIB", "reviewer": "Reviewer_JTzm", "summary": "This paper introduces a method called IFMET to perform multi-hop factual edits to language models. It first conducts an analysis of LM factual recall in the presence of multi-hop edits and finds that LMs integrate hops beyond the second in deeper MLP layers, compared to single-hop facts which are retrieved in shallow MLP layers. It then introduces IFMET, a two-stage edit process that propagates to multi-hop facts by first editing the first-hop fact at shallow MLP layers, then editing the subsequent-hop facts at deeper MLP layers. It uses a search-then-edit process in both stages, first locating where the nth-hop fact is stored using a n-hop edit prompt, then editing the MLP at that location to store the new fact. Experimental results show that IFMET significantly improves performance on multi-hop factual recall tasks compared to previous locate-then-edit methods. Ablation studies further confirm the importance of both stages of editing, the use of multi-hop prompts, and the targeting of deeper MLP layers for effective multi-hop knowledge editing.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> Response to Q1.1 in Claims And Evidence.\n\nIn our actual experiments, we conducted a simple exploration, and found that the probability of articles, e.g. *the*, *a* will increase significantly in the last few layers, which will squeeze the absolute probability of the answer to a certain extent. But the answer token is still in the position with the highest probability, so it will be output in the last layer. \n\n> Response to Q1.2 & Q1.3 & Q1.4 in Claims And Evidence about the causal intervention experiments.\n\nFirst of all, we need to state that we reported the absolute probability change in the paper. This was our oversight. Based on your and reviewer ixiZ’s suggestions, we revised the causal intervention method in the paper. We re-conducted a detailed intervention experiment with a window size = 5 to explore **the the role of layer hidden state and components like MLP and ATTN at different positions of prompt** with the decrease of probability in the last prediction layer as the causal effect *IE*.  The experimental results are as follows:\n1. Prompt position: We compared last subject token position (which is also generally considered important), the last token of first hop and the last token position. **Any intervention effect at the last subject token position (<3%) and last token of fist hop (<2.5%) was weak**  at the layer level or at the attention head and mlp levels. The response at the last token position was significantly stronger with **largest response over 12%** in deep layers.\n2. Layer intervention: We reached a consistent conclusion that the highest decay occurred in the **16-21th** layer, whose largest effect about **12%** on prob. We compared the strength of the average causal effect mentioned in figure 2 and 3 of the paper[1]. The intervention effect ranged from about 2.5% to 15%. We believe that such strength on prob and logit is acceptable. \n3. Component: We compared the effect of intervening the input of the attention head and the MLP on answer prediction. **The effect of the MLP is significantly better than that of the ATTN head, generally reaching more than 12% in the deep 18th-24th layers**, while the ATTN is less than 3% over all layers.\n4. In general, the results of the intervention experiment are consistent with the phenomenon observed in Figure 2. The implicit subject information plays a causal role in the generation of the final answer through the deep MLP.\n\n[1] Locating and Editing Factual Associations in GPT\n> Response to Q2 in Questions For Authors about the performance of pre-type tasks.\n\nSorry for that our definition of **pre and post type** in the Table 4 is ambiguous. In the paper, we have added the cases of editing the first n consecutive hops into the **pre** category, such as editing the first and second hops in 3 hops. But as you said, the important insight is to divide according to whether only the first hop is edited, so we reclassified the results into **pre and post type**, corresponding to the modification of only the first hop (i.e. explicit hop) or including n>1 hops (implicit hop) in correspondence with our conclusions in the exploration section and esperiment results in Table 2. After the new classification, there are 426 pre-type cases and 2574 post-type cases, the results in Table 4 under the new classification are as follows:\n\n\n| Editor |  Average↑(Edited Answer) | Pre↑ | Post↑ |  Average↓ (Undited Answer)| Pre↓ | Post↓|\n| -------- | -------- | -------- | -------- | -------- | -------- | -------- |\n| Base     | 7.70     |            11.97    | 6.99 | 39.63 | 34.04 | 40.56 |\n| Base+CoT | 6.83     |            8.22     | 6.60 | 42.83 | 41.31 | 43.08 |\n| PMET     | 11.17    |            39.20    | 6.53 | 29.95 | **10.09** | 33.26 |\n| PMET+CoT | 17.04    |            43.66    | 12.63| 29.35 | 12.67 | 32.13 |\n| IFMET    | 23.04    |            38.03    | 20.55| 23.08 | 11.27 | 25.02 |\n| IFMET+CoT| **31.01**    |            **43.66**    | **28.90**| **21.32** | 10.80 | **23.08**|\n\nIt can be seen that the previous method itself achieved significant results on pre type, as we expected, and our method is basically on par with it (this is predictable because the knowledge in the early MLP is fully updated in both cases). On post type, our method shows obvious superiority. This further confirms our findings in the paper. We will modify this in the paper, which will greatly improve the consistency and clarity of our article.\n\n> Response to Q1 in Questions For Authors & Q1.5 in Claims And Evidence about n-hop\n\nThe reason why we temporarily conducted experiments on 2-hop is partly because we followed the basic settings of previous work; on the other hand, the performance of the model for three-hop and four-hop in the case of cloze-prompt (being able to directly answer the final answer in the form of the highest probability token) is not ideal. Due to the limitations of data volume, existing models, and computing resources, we hope to explore this in future work.", "claims_and_evidence": "Examining the claims made and the evidence presented\n1. Multihop facts accumulate at the last token position compared to single-hop facts. This is supported by logitlens finding that the 2nd-hop fact subject peaks in the middle layers of the last token, while the single-hop fact subjects do not peak at all at the last token. It is also supported by a causal analysis: modifying the representation at that layer (/at those MLPs) to erase the subject of the 2nd-hop fact decreases the probably of the correct answer, compared to erasing a random token.\n    1. Figure 2 shows that the final answer probability actually decreases in the last few layers (beyond layer 25) which seems questionable to me. I expect the final answer probability to peak at the last layer?\n    2. The effect size is small for the causal analyses: the probability of the final token decreases by only 2.5% when the 2nd-hop fact is erased in the residual stream, and on the order of 1e-6 when it is erased in the MLP. This makes me wonder if the information is more distributed than the authors claim here. It's also questionable to me why, in the MLP version of this analysis, the effect is measured at the *residual stream of the layer*, rather than the final output...\n    3. While the results *do* show the final token has more information about the 2nd-hop fact compared to the 1st-hop fact, and that removing this information has a small degree of causal effect on the final answer compared to removing a random token, it's unclear whether this information is actually localized to the final token rather than an intermediate token. The authors should do some comparison on effect sizes through different positions in the prompt.\n    4. Furthermore, the claim that multi-hop facts are accumulated into/applied at the MLP layers should be checked, especially because of the relatively small effect size. Other layers, like attention, should be compared against.\n    5. Experiments are run on 2-hop facts but are used to generalize n-hop facts. Are similar results observed in n-hop facts?\n2. An implicit assumption is that the multi-hop fact *should* change if a fact is edited. It may be good to contextualize when this would be desired: for example, if the capital of Spain changes to Hartford, then it could be possible that we are in a counterfactual world where Spain and Connecticut switched names, so Pablo Picasso was actually born in \"Connecticut\" which still has capital \"Madrid\". Of course, it is more likely is that the capital of Spain changed its name.\n3. IFMET outperforms prior methods at multi-hop edits. This seems well-supported by performance improvements on multi-hop editing facts compared to baselines, especially PMET, and ablation studies show the necessity of each part of the method.", "methods_and_evaluation_criteria": "The method generally makes sense: MLP layers are modified for both the single-hop and multi-hop versions of prompts. MQuAKE-3K, a multi-hop knowledge editing benchmark, was used for evaluation. It should be clarified whether the same multi-hop prompts were used during editing as during evaluation, e.g. it seems important to validate that the edits generalize to when the fact is present alongside in a multi-hop chain with facts not seen during editing (e.g. if we change Spain's capital to \"Hartford\", then if its multihop edit was done using the prompt \"Pablo Picasso's country's capital is Hartford\", then it should be evaluated on the prompt \"the capital of the 4th most populous country in Europe is Hartford\" or even \"Hartford is in Europe\"). In general, IFMET appears to be an effective method for editing multi-hop facts, and does not seem to negatively impact single-hop facts. However, there are several limitations of this method compared to prior work, especially because more edits need to be made, for example:\n1. Negatively impacting specificity (Table 8) indicating that other unrelated facts are being affected by these edits\n2. Being more expensive because of the need to make more edits (Table 9)\n3. Assuming access to a knowledge base (e.g. WikiData/SPARQL) in order to construct multihop prompts to use for editing, which prior methods do not.", "theoretical_claims": "No theoretical claims in this paper.", "experimental_designs_or_analyses": "The experimental designs generally seem sound. The authors perform evaluation against baselines on MQuAKE and compare results based on # of edits and # of hops. There is also comprehensive ablation analysis on each component of the IFMET method, showing that both stages are necessary, and using multi-hop prompts at deeper MLP layers are necessary.", "relation_to_broader_scientific_literature": "The authors compare against essential prior work on model weight editing like MEND, ROME, MEMIT, PMET, as well as finetuning. They show improvements against these baselines on multi-hop knowledge edits on an established benchmark, MQuaKE. They make an interesting discovery that multi-hop information is stored in later layers than single-hop information, and accumulated at the last token, which builds upon prior understanding of where single-hop knowledge is stored.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "See strengths and weaknesses above.\n\nIn addition, some clarity suggestions below:\n1. Important methodological details about IFMET is allocated to the appendix, as well as context for understanding the metrics used in Table 4. These should be moved up, and perhaps some details about the ablations and generalizability can be allocated to the appendix.\n2. The paper should -- early on -- clarify whether what matter is the *total* number of hops in the edit, or how many hops deep the *edited fact* is. For example, it is my understanding that editing the first fact in the chain is akin to a \"single-hop\" edit (and is stored in shallow MLP layers), while editing the nth fact in the chain is akin to a multi-hop edit (and is stored in deep MLP layers). So it doesn't matter how long the multi-hop chain is in total, just the (absolute?) position of the edited fact in the chain. This should be clarified early on.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "1. How are 3/4-hop facts stored? Are they all stuck in the same MLP layer past the first hop? Or is there a smooth correlation of depth and position in the edit chain?\n2. \"Even in the pre-type tasks, where previous methods are relatively more proficient, IFMET achieves a significant improvement.\" (L385 right column) Why is this the case?", "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": "NuoFwBu3v9", "reviewer": "Reviewer_1ooJ", "summary": "This paper investigates how LLMs handle multi-hop factual recall under knowledge editing. Using various interpretability methods, the authors uncover a critical insight: for multi-hop questions, LLMs rely primarily on implicit subject information encoded in deeper MLP layers to derive final answers. This mechanism differs significantly from that of single-hop tasks, explaining the failure of previous locate-then-edit methods when applied to multi-hop factual recall. Building on these findings, they introduce IFMET, a novel locate-then-edit knowledge editing method. Besides editing the shallow MLP layers with single-hop edit prompts, IFMET employs multi-hop edit prompts to precisely locate knowledge requiring modification and subsequently edits the deeper MLP layers. Comprehensive experimental evaluation demonstrates that IFMET substantially outperforms other methods.\n\nupdate after rebuttal\n\nMy concerns have been addressed, and I maintain my score.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your recognition of our work and for your suggestion to extend the framework to other editing methods (such as **MEMIT**) to demonstrate the generalization of the method itself. We conducted detailed experiments to verify this point. Due to time constraints, we tested it on the ablation set (a subset of 500 items consistent with the ablation experiment). The experimental results on **MEMIT** are as follows\n\n\n| Editor | Multi-hop | Efficacy |  Specificity | Paraphrase\n| -------- | -------- | -------- | -------- |  -------- |\n| MEMIT     | 15.6     | 93.0    | 76.6| 76.4 |\n| IFMET(with MEMIT) | 24.2 | 100 | 67.2 | 82.2\n\nIt can be seen that using **MEMIT** as the baseline and using **PMET** as the baseline (Table 7) have similar trends in the four metrics of **Multi-hop Acc, Efficacy, Specificity, and Paraphrase**, which proves that our framework is generalizable on different editing baselines.", "claims_and_evidence": "The claims made in the submission are supported by clear and convincing evidence", "methods_and_evaluation_criteria": "Experimental results show that IFMET exhibits superior performance.", "theoretical_claims": "Yes, The theories of logitlens and causal intervention methods used in mechanism exploration have been checked.", "experimental_designs_or_analyses": "The validity and rationality of the experimental design and analysis of the IFMET method are mainly demonstrated through experiments on the answer accuracy of the original answers and the edited answers, as well as comparative experiments across multiple models and ablation studies.", "relation_to_broader_scientific_literature": "Knowledge editing is an important research field, and the analysis of reasoning mechanisms and the improvement of methods in knowledge editing scenarios are very valuable.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Strengths:\nClear Structure and Precise Expression: The quality of the writing is good. Complex concepts and intermediate solutions are effectively expressed through well-chosen examples and informative visualizations.\n\nNovel Insight: The paper highlights the limitations of current knowledge editing methods and provides valuable guidance for future research. The analysis of reasoning mechanisms in knowledge editing scenarios and the discovery of implicit subject information in intermediate layers contribute significantly to our understanding of LLM reasoning.\n\nStrong Theoretical Support of Method: The authors have constructed a rigorous logical chain through extensive exploratory experiments and theoretical derivations, making the logical foundation of the proposed methodology, IFMET, both solid and well-supported, with high interpretability.\n\nExperiments: IFMET improves both original and new answers, has been tested across various models, and demonstrates its effectiveness. The authors also provide a detailed discussion of generalizability.\n\nWeaknesses:\nThe IFMET framework proposed is based on existing knowledge editing methods, but seems to have been experimented on PMET, hoping to be able to extend the framework to other editing methods (such as MEMIT) to demonstrate the generalization of the method itself.", "other_comments_or_suggestions": "Refer to weakness.", "questions_for_authors": "Refer to weakness.", "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": "rfGHVwYQzj", "reviewer": "Reviewer_ixiZ", "summary": "This paper extends the *locate-then-edit* approach of model editing proposed by [Meng et al, 2023](https://arxiv.org/pdf/2202.05262) to multi-hop factual recall tasks. During the localization experiments the authors find that: on multi-hop factual recall tasks, the LM retrieves implicit subject information in the *deeper* MLP layers; which the paper claims as the key reason why existing knowledge editing methods targeting shallower MLP layers do not generalize for multi-hop factual reasoning tasks. With this finding, the authors propose IFMET, a new 2-step knowledge editing approach that edits shallower MLP layers first for single-hop fact recall tasks, and then edits deeper MLP layers for multi-hop factual recall tasks.\n\n## update after rebuttal\nI thank the authors for their detailed response to my questions. And I would like to keep my score as is.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your recognition of our work. Here is our response.\n\n> Response to Q1.a in Questions For Authors\n\nIn the past exploration process, we also used patchscope to perform the experimental effects shown in figure 2, and judged whether the corresponding information was contained by counting whether the subsequent output of patchscope contained the answer. During the experiment, the results were surprisingly consistent with logitlens, so considering the universality of the logitlens method and cost-effectiveness, we still use it as our interpretability tool. We believe that the results obtained by logitlens are already trustworthy, but in the future, we can continue to explore further through methods such as tuned-lens.\n\n> Response to Q1.b in Questions For Authors\n\nFirst,regarding the optimization of s2 related information, we did not use SGD optimization, but replaced the logits of the token in s2 with the minimum value on the whole vocabulary. Here it should be expressed as $s_l^*[j]$ = min($s_l$) in equations 1, where $s_l$ represents the all tokens in vocabulary, and then a simple mathematical calculation is performed to get the corresponding hidden state with a combination of least squares and minimum-norm methods.\nAnd, we are sorry that we omitted explicit description of the Equation 2 in the paper. Your understanding is completely correct. We did not take all j∈o2 tokens into account. In practice, we only consider the first token in o2. As you said, this is enough for the purpose of this localization experiment.\n\n> Response to Q1.c in Questions For Authors\n\nCorresponding to the example you gave, it is a two-hop fact recall chain. It includes (the louvre, located country, france) + (France, capital, Pairs). Instead of using a counterfactual example to perform a conventional corruption, we refer to [1] and reduce the amount of information about **France** in the last token position by minimizing its corresponding logits value. Then, we observe the causal effect of **France, the implicit intermediate subject,** by observing the decrease in the probability of **Paris**.\nAnd the \"invent\" is a spelling error, and we will correct it in the camera-ready version.\n\nRegarding the issue you mentioned about the significance of causal effects, we have provided a detailed explanation in the section \"**Response to Q1.2 & Q1.3 & Q1.4 in Claims And Evidence about the causal intervention experiments**\" in our reply to **Reviewer JTzm**. Please refer to that section for more details.\n\n[1] Understanding and Patching Compositional Reasoning in LLMs\n\n> Response to Q2 in Questions For Authors\n\nWe construct a multi-hop prompt to put the knowledge to be modified into the implicit reasoning step. While mquake only provides a single edit template, we give a practical example here.\n\n\n| Edit Case | Single-hop Prompt in MQuAKE | Test multi-hop Prompt in MQuAKE | Our multi-hop Prompt | \n| -------- | -------- | -------- | -------- |\n| (Marc Cherry,cizizen,United States of America ->  Bulgaria)  | Marc Cherry is a citizen of | Which country is the creator of \\\"Devious Maids\\\" a citizen of?     | The creator of Desperate Housewives is a citizen of    |\n\nWe use all the relationships that have appeared in MQuAKE to construct possible multi-hop edit prompts and then delete those that have the same explicit subject as the test multi-hop question, keeping the best one that the model can successfully answer.\n\nIn Table 3, the other methods we reported only used single-hop editing prompt, and only IFMET used our framework. For the application of multi-hop prompt in other methods, please refer to the section in the reviewer 1ooJ's response. We reported in detail the improvement achieved by applying our framework to **MEMIT**, which illustrates the generalization of our method for editing baselines. \n\n> Response to Q3 in Questions For Authors about the CoT.\n\nWe are sorry that our examples are confusing to you. We may not have explained them clearly enough. As you said, our cot setting is to cut the prompt after `Thoughts:`, so that the LM has to generate the thought itself (pressured by the prompt and ICL examples). For the same multi-hop problem, we generate cot and its corresponding answers three times to ensure that possible errors are avoided. We will correct this in the new version PDF.", "claims_and_evidence": "I have several concerns on the localization experiments (Section 3.1) and evaluation of IFMET. Please see my questions.", "methods_and_evaluation_criteria": "The authors use MQuaAKE [Zhong et al, 2024](https://arxiv.org/pdf/2305.14795), which is a recognized benchmark for multi-hop factual recall tasks, to evaluate their method. However I have some concerns about the evaluation methodology. See questions.", "theoretical_claims": "The paper is empirical in nature, and does not contain any strong theoretical claims.", "experimental_designs_or_analyses": "I think using Logit Lens as a tool to identify key locations/states in the LM computation has a systematic bias towards the later layers. See questions for details.", "relation_to_broader_scientific_literature": "Previous works investigating factual recall mechanisms on LMs attributed knowledge retrieval (also known as enrichment, detokenization, ...) to MLPs is shallower layers, at the subject last token position ([Meng et al, 2023](https://arxiv.org/pdf/2202.05262), [Geva et al., 2023](https://arxiv.org/abs/2304.14767)). This paper extends the setting to multi-hop factual recall tasks, and finds that the LM retrieves implicit subject information in the *deeper* MLP layers, at the last token position. The paper further shows that a knowledge editing method designed with this insight is more effective in multi-hop factual recall than previous methods targeting shallower MLP layers.", "essential_references_not_discussed": "Two relevant papers comes to my mind.\n\n1. [Geva et al., 2023](https://arxiv.org/abs/2304.14767) -- investigates single-hop factual recall mechanism in LMs.\n2. [Merullo et al., 2023](https://arxiv.org/abs/2305.16130) -- also investigates mostly single-hop factual recall. But the paper finds that sometimes the subject entity is being recalled again at the last token position of the prompt.\n\nJust to be clear, I don't feel strongly about either of these papers. I thought that they are relevant to the paper and would leave it to the authors to decide whether to cite them.", "other_strengths_and_weaknesses": "**Strenghts:** Well-motivated paper -- addresses a key problem in knowledge editing in LMs.   \n**Weaknesses:** See questions.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "1. **Localization of key states and modules** \n\n    a. Section 3.1 extensively uses Logit Lens ([Nostalgebraist, 2020](https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens)) as a mechanistic interpretability tool to identify key locations/states in the LM computation during a multi-hop factual recall task. However, Logit Lens can give noisy results in earlier layers ([Belrose et al., 2023](https://arxiv.org/pdf/2303.08112)) and only starts giving meaningful results in deeper layers ([Geva et al., 2024](https://arxiv.org/pdf/2401.06102)). Therefore, in my opinion, using Logit Lens as a localization tool in this case has a systematic bias towards deeper layers. I would love to hear the authors' thoughts on this.\n\n    b. I am not sure that I clearly understand the motivation behind the choice of Equations 1 and 2.\n    * In Equation 1, all $j \\in s_2$ are being optimized for. However, $W_U h$ is a linear operation. Thereby shouldn't the (probably weighted) mean of the rows of $W_U$ corresponding to $j \\in s_2$ be sufficient? Do we really need a SGD optimization here? \n    * Similarly in Equation 2, all $j \\in o_2$ are being considered. However, these LMs are autoregressive and will not generate all $j \\in o_2$ at once. Therefore, shouldn't we only consider the first token in $o_2$? I understand that the first token is not always enough to identify the correct generation, but it should be possible to curate a set of candidates where this is not the case, which I think is enough for the purposes of this localization experiment.\n\n    c. I am not really sure I understand the results of Figure 3. Probably having the prompts on which the intervention is being performed would help. Here's my guess about the setup, please correct me if I am wrong.\n\n    * The multi-hop prompt is something like `\"The capital of the country where {} is located is\"`. `{}` is replaced with $s_2 = \\texttt{the Louvre}$ and the answer is $o_2 = \\texttt{Paris}$. Then $s_l^*$ is optimized for a different $s_2 = \\texttt{the Statue of Liberty}$ and the intervention is being performed on corresponding states at the last token position of the prompt (?)\n\n    * What does \"invent\" label mean in Figure 3? Is it a typo for \"intervention\"? Also the probability *decrease* is being measured. Assuming the setup I understand is correct, is it measuring the probability decrease for `Paris` or increase for `Washington`?\n    \n    * The values in Figure 3 aren't very large, strongest effects around 0.025 for residual states and $7 \\times 10^{-6}$ for MLPs. Is this significant enough to draw any strong conclusions?\n\n\n2. **Evaluation**\n\n    a. The paper mentions that for IFMET the multi-hop prompt is constructed using the WikiData and the LM itself (Lines 309-10). How much do they differ from the prompts in MQuaAKE, on which the edit is being evaluated?\n\n    b. As the single edit performance of other methods reported in Table 3 is so low, I am assuming they are all being evaluated on multi-hop prompts. Is this correct? Then shouldn't this be reported as a grid shaped #Edits $\\times$ #hops matrix instead? \n\n    c. Looking at Table 7 in the Appendix, I wonder why the single edit efficacy of PMET below 90\\% despite it being a direct optimization approach? I also encourage the authors to also include ROME for single edits and MEMIT for mutiple edits in this comparison.\n\n\n3. When you incorporage CoT, did you make sure to not give the answer away? The example in Table 10 has the answer in the thought process. \n    \n    One way you can easily do that is cut-off the prompt before the answer. In your example it would be `\"... Chevrolet is owned by\"`, But then I think the single hop editing should also be effective here. However, probably that is not what you do as you report poor performance of single hop editing in Tables 3 and 4.\n\n    Alternatively, you can cut the prompt after `Thoughts:`, so that the LM has to generate the thought itself (pressured by the prompt and ICL examples). \n\n    The setup wasn't clarified in the paper (if I didn't miss it) and I think it's an important thing to clarify.", "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:

Meta-review: This paper studies multi-hop knowledge editing, using mechanistic interpretability methods to identify failures of existing editors in multi-hop knowledge editing. They then use these insights to develop a new editing method that makes multi-hop knowledge edits by conducting edits in two stages. Their approach outperforms the state-of-the-art on multi-hop knowledge editing tasks.

Reviewers ultimately felt the strengths of this work outweigh the weaknesses. They particularly appreciated the work's clarity, useful insights, problem importance, empirical analysis, and resultant performance of the method.
Decision: Accept (poster)