Can Knowledge Editing Really Correct Hallucinations?
Authors: Baixiang Huang
Canyu Chen
Xiongxiao Xu
Ali Payani
Kai Shu
Venue: ICLR 2025 Poster (2025)
Keywords: LLMs
Knowledge Editing
Hallucination
Benchmark
Primary area: foundation or frontier models, including LLMs
TL;DR: We proposed HalluEditBench to holistically benchmark knowledge editing methods in correcting real-world hallucinations on five dimensions including Efficacy, Generalization, Portability, Locality, and Robustness.

Abstract: Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual information in generated content, despite their superior capacities across tasks. Meanwhile, knowledge editing has been developed as a new popular paradigm to correct  erroneous factual knowledge encoded in LLMs with the advantage of avoiding retraining from scratch. However, a common issue of existing evaluation datasets for knowledge editing is that they do not ensure that LLMs actually generate hallucinated answers to the evaluation questions before editing. When LLMs are evaluated on such datasets after being edited by different techniques, it is hard to directly adopt the performance to assess the effectiveness of different knowledge editing methods in correcting hallucinations. Thus, the fundamental question remains insufficiently validated: Can knowledge editing really correct hallucinations in LLMs? We proposed HalluEditBench to holistically benchmark knowledge editing methods in correcting real-world hallucinations. First, we rigorously construct a massive hallucination dataset with 9 domains, 26 topics and more than 6,000 hallucinations. Then, we assess the performance of knowledge editing methods in a holistic way on five dimensions including Efficacy, Generalization, Portability, Locality, and Robustness. Through HalluEditBench, we have provided new insights into the potentials and limitations of different knowledge editing methods in correcting hallucinations, which could inspire future improvements and facilitate progress in the field of knowledge editing.

Published as a conference paper at ICLR 2025
CAN KNOWLEDGE EDITING REALLY CORRECT HALLUCINATIONS?
Baixiang Huang"), Canyu Xiongxxao Xu², Ali Payani', Kai Shu
'Emory University, 'Ilinois Institute of Technology. CCicoo Research (baixiang. huang, wetigg,kiissssssee.. shu)@emory, edu, (cchenc xxu85 JChawk. edu, apayani@ciscoo.com
Project website: https://1mm-ee wew.eee.e.iiiiniiiiiiiteeee githbb.
ABSTRACT
Large Language Models (LLMS)) suffer from hallucinations, referring the factual information in generated content, despite their superior capacities across tasks. Meanwhile, knowledge editing has been developed a new popular paradigm to correct erroneous factual knowledge encoded in LLMs with the ad- vantage of avoiding retraining from scratch. However, common issue of existing evaluation datasets for knowledge editing is that they do not ensure that LLMs actually generate hallucinated answers the evaluation questions before edit- ing. When L.MSs are evaluated such datasets after being edited by different techniques, is hard to directly adopt the performance to assess the effectiveness of different knowledge editing methods in correcting hallucinations. Thus, the fun- damental question remains insufficiently validated: Can knowledge editing really correct hallucinations in LLMs? We proposed HalluEditBench to holistically benchmark knowledge editing methods in correcting real-world hallucinations. First, rigorously construct massive hallucination dataset with 9 domains, 26 topics and more than 6, 000 hallucinations. Then, we assess the performance of knowledge editing methods in holistic way five dimensions including Efficacy, Generalization, Portability, Locality, and Robustness. Through HalluEditBench. we have provided new insights into the potentials and limitations of different knowledge editing methods in correcting hallucinations, which could inspire future improvements and facilitate progress in the field of knowledge editing.
INTRODUCTION
Large Language Models (LLMs) have shown superior performance in various tasks (Zhao et al., 2023). However, one critical weakness that they may output hallucinations, refer- ring to the non-factual information in generated content, for reasons such as the limit of mod- els' internal knowledge scope or fast-changing world facts (Zhang et al., 2023). Considering the high cost of retraining LLMs from scratch, knowledge editing has been designed as a new paradigm to correct erroneous outdated fac- tual knowledge in LLMS (Wang et al., 2023c).
Method
WikiDData.com
ZsRE
WikiBio
Pre-edit
47.40
37.49
61.35
Post-cdit (ROME)
97.37
96.86
95.91
Post-edit (MEMIT)
97.10
95.86
94.68
Post-edit (FT-L)
56.30
53.82
66.70
Post-cdit (FT-M)
100.00
99.98
100.00
Post-edit (LoRA)
100.00
100.00
100.00
Table 1: Performance measured by Accuracy (%)
of Llama2-77 before editing ("Pre-edit") and after applying typical knowledge editing methods ("Post- edit") on common existing evaluation datasets.
Although there are many existing question-answering datasets such as WikiDatareenn (Cohen et al., 2024), ZsRE (Yao et al., 2023), and WikiBio (Hartvigsen et al., 2024) widely used for the evaluation of knowledge editing, one common issue that they do not verify whether LLMs, before applying knowledge editing, actually generate hallucinated answers to the evaluation questions. When such datasets are adopted to evaluate the performance of L.LMs after they have been edited, hard to directly use the scores to judge the effectiveness of different knowledge editing techniques in correcting hallucinations, which is the motivation of applying knowledge editing to LLMs.
To better illustrate this point, following the evaluation setting in Zhang et al. (2024f), we conducted a preliminary study to examine the pre-edit and post-edit performances of Llama2-7B on the three
"Equal Contribution. 'Corresoonding author.

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Ilyo (Hallucination!)
Who the Chief Scientist OpenAt?
Jakub Pachocki
Who is the Chief Scientist of OpenAI?
Efficacy
Jokub Pachocki
Who acts os the Chief Scientist OpenAT?
X
Yes
Is Jakub Rachocki the Chief Scientist of OpenAI?
Knowledge Editing
No
Is Sutskever the Chief Scientist of OpenA17
Generalization
Who Chief Scientist OpenAI?
B.
A, Tlyo Sutskever B, Jokub Pachocki C. Sam Altmon
OpenAl
Of which compony is Jokub Pachocki the Chief Scientist?
Where is the Chief Scientist of OpenAI born?
Portability
Sam Altman
Who is the CEO of OpenA1?
Locality
Yes
original question wrong. Jakuub Pachocki Robustness the Chief Scientist of OpenAl? Respond with "Yes" or "No".
Figure 1: Framework of HalluEEitBench. For real-world hallucinations, holistically assess the performance of knowledge editing on Efficacy. Ceneralization, Portability, Locality, and Robustness.
aforementioned evaluation datasets. As shown in Table 1, we clearly observe that Llama2-7B achieves relatively high performance, measured by the rate of answering the evaluation questions correctly (Accuracy (%)), even before applying knowledge editing techniques. Although the knowl- edge editing methods can bring an increase in accuracy, the high post-edit performance on these datasets cannot faithfully reflect the true effectiveness correcting real-world hallucinations and may cause distorted assessment. Thus, the fundamental question remains insufficiently validated: Can knowledge editing really correct hallucinations in LLMs?
To fill in the essential gap in the field of knowledge editing, we propose HalluEditBench to holisti- cally benchmark knowledge editing techniques in correcting real-world hallucinations of L.MMs. As shown in Figure 1, the construction of Hallu FalluEhditeench can generally be divided into two phases. In the first phase, we constructed a massive hallucination dataset encompassing domains and topics based Wikidata. For each of Llama2-7B, Llama3-8B, and Mistral-vO.3-7B, we have rigorously filtered more than 10 thousand hallucinations accordingly. In the second phase, we sampled around 000 hallucinations for each LLM covering all the topics and domains, and then generated evalua- tion question-answer pairs from five facets including Efficacy, Generalization, Portability, Locality, and Robustness. Through extensive empirical investigation on performance of 7 typical knowledge editing techniques, including FT-L (Zhu et al., 2020; Meng et al., 2022), FT-M (Zhang et al., 2024f), MEMIT (Meng al., 2023), ROME (Meng al., 2022). LoRA (Hu et al., 2022), ICE (Zheng et al., 2023), and GRACE (Hartvigsen et al., 2024), regarding the aforementioned five dimensions, we have provided novel insights into their potentials and limitations. summary of the insights is as follows:
The effectiveness of knowledge editing methods in correcting real-world hallucinations could be far from what their performance on existing datasets suggests, reflecting the potential unreliability of previous assessment of different knowledge editing techniques. For example, although the performances of FT-M and MEMIT Table are close to 100%, their Efficacy Scores in HalluEditBench are much lower, implying the likely deficiency in correcting hallucinations.
No editing methods can outperform others across five facets and the performance beyond Efficacy for all methods is generally unsatisfactory. Specifically, ICE and GRACE outperform the other five methods on three LLMS regarding Efficacy. All editing methods except ICE only slightly improve negatively impact the Generalization performance. Editing techniques except ICE could even underperforn pre-edit LLMs on Portability. FT-M and ICE surpass others on Locality performance. ICE has poor Robustness performance compared other methods.
The performance of knowledge editing techniques in correcting hallucinations could highly depend on domains and LLMs. For example, the Efficacy performances of FT-L across LLMs are highly distinct. Domains have large impact on the Locality performance of ICE.
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Figure 2: Statistics of HalluEditBench Across Topics and Domains.
2 HalluEditBench: HOLISTICALLY BENCHMARKING KNOWLEDGE EDITING METHODS IN CORRECTING REAL-WOOLLD HALLUUINAATIONS
In this section, we will introduce the details of HallnnEditBench, including the construction of the massive LLM hallucination dataset, the generation of evaluation question-answerin -answering pairs from five dimensions, evaluation metrics and the benchmarked knowledge editing techniques.
2.1 HALLUCINATION DATASET CONSTRUCTION
The goal of knowledge editing can generally be defined as transforming existing factual knowledge in the form of a knowledge triplet (subject s, relation object o) into a new one (subject relation r, object o²). These two triplets share the same subject and relation but have different objects. A knowledge editing operation can be represented as e = (s, r, o, o"). Considering one example of applying knowledge editing to correct hallucinations in LLMs, given a factual question "Who is the Chief Scientist of OpenAI?". LLMS may respond with "Ilya Sutskever", which is factually incorrect due to the outdated information contained L.LMs. The editing operation can be e = (s = OpenAI, Chief Scientist, o Ilya Sutskever, o Jakub Pachocki). The successfully edited LLMs are expected to answer "Jakub Pachocki" " rather than "Ilya Sutskever". Thus, we need to collect large scale of knowledge triplets and factual questions to filter hallucinations.
Following existing editing datasets (e.g.. WikiDattauentt (Cohen et al., 2024) and WikiBio (Hartvigsen al., 2024)), we also choose Wikidata the factual knowledge source. In the first step, we retrieved 143, 557 raw knowledge triplets using the Wikidata Query Service (Query date: September 8th, 2024) from 26 topics, which can be categorized into 9 domains including art, business, entertainment, event, geography, health, human. places, and technology. Each topic has at least 100 triplcts. In the second step, we filtered out the triplcts that share the same subject and relation while the objects are different, indicating there are more than one answers to questions about the object. When we construct factual questions and compare LLM- -egeeeerededd answers with the objects of these triplets, it would be difficult to determine whether LLMs actually hallucinate the questions. For example, for two triplets (Canada, diplomatic relation, India) and (Canada, diplomatic relation, Greece), which share the same subject and relation, there are multiple answers to the question "What country has diplomatic relation with Canada?" In the third step, following Wang et al. (2024e), we applied rules to convert knowledge triplets into factual questions with objects the ground- truth answers. By comparing LLMM-generaed responses with the answers, we obtained a massive hallucination dataset. Specifically, we collected 12, 619, 13, 210, and 14, 366 hallucinations for Llama2-7B, Llama3-8B. and Mistral-vO.33 respectively. Finally, we sampled a subset of hallucinations covering all the topics and domains construct HalluEditBench. The distribution statisties are shown in Figure 2.
is worth noting that the hallucinations for different LLMs can have distinct patterns, which cannot be found on existing knowledge editing datasets since they do not verify whether L..M-generated answers are hallucinated before applying knowledge editing. We made the first attempt to investigate the performance of knowledge editing techniques on verified hallucinations of different LLMs.
2.2 EVALUATION QA PAIR GENERATION AND METRICS
After constructing the hallucination dataset, we propose holistically assess the performance of knowledge editing methods in correcting hallucinations from five facets including Efficacy, Gener- alization, Portability, Locality, and Robusiness. First, we leveraged GPT-4o to generate evaluation
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question-answerie pairs for each facet based on the hallucination dataset well as the factual verification questions in Section 2.1. Then we also manually inspect their quality. One example the evaluation QA pairs for each facet is shown in Figure (More examples are provided in Appendix F). The specific prompt design for GPT-4o is shown in Appendix A.
Then, we calculated five scores including Efficacy Score (%), Generalization Score ( (%), Portability Score (%), ) Locality Score (%), and Robustness Score (%) based on the evaluation QA pairs to measure the performance of different editing methods. Except that Locality Score is defined as the unchanging rate of LLMs' responses after editing on Locality Evaluation Questions, the other scores are calculated by accuracy on corresponding evaluation QA pairs. More details are as follows:
Facet 1: Efficacy Efficacy Evaluation Questions the same the factual verification questions in the hallucination collection to ensure the pre-edit performance is 0% regarding Efficacy Score. Thus, Efficacy Scores of post-edit LLMs can directly reflect the effectiveness in correcting hallucinations.
Facet 2: Generalization The Generalization Scores aim to evaluate the capacity of LLMs in answering different questions regarding the same knowledge triplet, suggesting the generalization of edited knowledge in diverse scenarios. As shown in Figure we propose five types of Generalization Evaluation Questions including "Rephrased Questions", "Yes-or-No Questions" with "Yes" or "No" answers, AMulti-Choice Questions", "Reversed Questions". We have calculated the Generalization Scores for each type and also provided averaged Generalization Scores across five types.
Facet 3: Portability The Portability Scores intend to measure the ability of LLMs to reason about the downstream effects of edited knowledge. Thus, design the Efficacy Evaluation Questions with N hops (N = 6) as Portability Evaluation Questions. When = 2, the example is shown in Figure 1. When the answer to the question "Who is the Chief Scientist of OpenAI?" changes from "Ilya Sutskever" to "Jakub Pachocki", the answer to the downstream question "Where is the Chief Scientist of OpenAI born?" should also change from "Russia" to "Poland".
Facet 4: Locality The Locality Scores quantify the side effect of knowledge editing on unrelated knowledge. We designed Locality Evaluation Questions related to the subject but irrelevant to the object in the original triplet, which can be "Who is the CEO of OpenAI?" for the aforementioned example. Then, we calculate the rate of keeping the same answer after editing as Locality Scores.
Facet 5: Robustness We proposed Robustness Scores to assess the resistance of edited knowledge in LLMs against external manipulations. Although the literature has studied the general sycophancy behavior of LLMs (Sharma et al., 2024b), the robustness of edited factual knowledge against users' distractions (e.g., "Your answer to the original question is wrong. ") is under-explored. After post-edit LLMS are tested with Efficacy Evaluation Questions, we further prompted them with Robustness Evaluation Questions, which are exemplified Figure for M turns (M 1 ~ 10) and calculated the rate of "Yes" for each round as the Robustness Scores, reflecting the extent to which LLMs insist on the corrected knowledge. Then, we can investigate the robustness differences of edited knowledge in LLMs when applying diverse editing techniques.
2.3 KNOWLEDGE EDITING TECHNIQUES
We propose to categorize the majority of existing knowledge editing techniques into the following types and chose representative techniques (more details are in Appendix B) in HalluEditBench.
Locate-then-edit is popular knowledge editing paradigm that first locates factual knowledge at specific neurons or layers, and then makes modifications on them directly. We selected two typical methods ROME (Meng et al., 2022) and MEMIT (Meng et al., 2023) in HalluFdittBechh.
Fine-tuning is a simple and straightforward way to update the parametric knowledge of LLMs. We selected three variations FT-L (Meng al., 2022), FT-M (Zhang et al., 2024f), and LoRA (Hu et al., 2022), which mitigate the catastrophic forgetting and overfitting issues of standard fine-tuning.
In-Context Editing is a training-free free paradigm that associates LLMs with in-context knowledge directly (Zheng al., 2023; Shi et al., 2024; Fei et al., 2024). We adopted simple baseline ICE method in Zheng et al. (2023) that puts the new fact context and does not require demonstrations.
Memory-based methods usually maintain a memory module for knowledge storage and updating. We selected a typical technique GRACE (Hartvigsen et al., 2024), which manages discrete codebook and does not modify the original parameters. When encountering queries about edited knowledge, an adaptor adjusts layer-to-layer transformations with values searched in the codebook.
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Figure 3: Efficacy Scores of Knowledge Editing Methods. The "overall" refers to the Efficacy Score (%) the whole HalluEdittench embracing 9 domains for different methods. The Efficacy Score on each domain is also reported. Efficacy scores (%) are measured by the accuracy on Efficacy Evaluation Question-answw Pairs, where the pre-edit scores of each LLM are ensured 0%.
RESULTS AND ANALYSIS
In this section, comprehensively analyze the experiment results 9 domains and the overall performance on the whole HalluEEdiBBench for different knowledge editing techniques from five facets including Efficacy, Generalization, Portability, Locality, and Robustness.
3.1 FACET 1: EFFICACY
Since we have ensured that LLMs generate hallucinated answers to the Efficacy Evaluation Questions before editing, the pre-edit Efficacy Score for all editing techniques 0%. Thus, Efficacy Scores in Figure can directly reflect the effectiveness of different techniques in correcting real-world hallucinations. We find that the effectiveness of some techniques can be far from what their performance previous datasets suggests, implying the potential unreliability of their previous evaluation. For example, as shown in Table 1, although FT-M achieves near 100% performance in existing datasets such WikiData,seent ZsRE, and WikiBio, overall Efficacy Scores on Llama2-7B and Mistral-vO.3-7B are only around 60%. There is similar performance drop for MEMIT.
Second, based on the overall Efficacy Scores across three LLMs, the following effectiveness ranking generally holds: FT-L < FT-M MEMIT < ROME < LoRA < ICE < GRACE. We can observe that ICE and GRACE, which both preserve original weights LLMs, outperform the other methods, implying the potential disadvantage of directly modifying parameters for knowledge editing.
Third, we notice that efficacy scores of knowledge editing techniques could highly depend on domains and LLMSs. For example, the scores of FT-L on different domains and LLMs could be highly distinct. The performance of FT-L and FT-M on Llama3-8B higher than that on Mistral-v0.3-7B,
Insight 1: (1) The current assessment of knowledge editing could be unreliable; (2) ICE and GRACE outperform parameter-modifying editing techniques such as fine-tuning and "Locate- then-Edit" methods on Efficacy; (3) Domains and LLMs could have a high impact on Efficacy.

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Figure 4: Generalization Scores of Knowledge Editing Methods. Generalization Scores (%) are measured accuracy five types of Generalization Evaluation Questions including Rephrased Questions ("rephrase"), Yes-or-No Questions with "Yes" or "No" as answers ("yes" or "no"), Multi- Choice Questions ("me"), Reversed Questions ("reversed")). The "average" refers averaged scores over five question types. The figure only shows the overall Generalization Scores for each type on the whole HalluEdittBnchh. Generalization Scores for each domain are given in Appendix E.
3.2 FACET GENERALIZATION
As shown Figure 4, even though the pre-edit Efficacy Score performances for different editing techniques three LLMs ensured 0%. it is worth noting that the pre-edit Generalization Score performance is not 0% for each question type, illustrating that the manifestation of hallucination actually depends the design of question prompts. Given group of diverse question prompts for the same knowledge triplet, LLMS may hallucinate some questions but answer others correctly.
Surprisingly, find that post-edit Generalization Scores could even be lower than pre-edit scores for the same LLM and question type. demonstrating the potential negative effect caused by knowledge editing. In more detail, we can observe clear performance drop for GRACE across all the question types, and for FT-L and LoRA on some question types.
Comparing the ranking of Efficacy Scores in Figure 3 with Figure 4, higher Efficacy Scores do not also necessarily indicate higher Generalization Scores. Especially, although GRACE almost surpasses all the other editing techniques regarding Efficacy Scores, largely degrades the Generalization Scores compared to pre-edit performance. In addition, all editing methods except ICE only slightly improve or even hurt Generalization Scores.
explicitly that
Insight 2: (1) The manifestation of hallucination depends on question design; (2) Higher Efficacy Scores do not also necessarily indicate higher Generalization Scores; (3) All editing techniques except ICE only slightly improve or negatively impact the Generalization performance.

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Figure 5: Portability Scores Knowledge Editing Methods. Portability Scores (%) are measured by the accuracy on Portability Evaluation Questions, which are Efficacy Evaluation Questions with N hops (N = 1 ~ 6). The Portability Evaluation Questions are the same as Efficacy Evaluation Questions when N is 1. The Portability Scores two domains "human" and "places" reported in the figure. The results for more domains are given in Appendix E.2. The "overall" refers to the Portability Score (%) the whole HalluFditBBech embracing 9 domains.
3.3 FACET 3: PORTABILITY
Figure demonstrates the pre-edit and post-edit Portability Scores for Portability Evaluation Ques- tions with N hops (N 1 ~~~ 6). When N 1, the Portability Evaluation Questions are the same as Efficacy Evaluation Questions, suggesting that the Portability Scores are O. Similar to Figure 4, we discover that the pre-edit Portability Scores are not zero for necessarily need to reason based on single-hop knowledge to answer multi-hop questions. We hypothesize that this is because LLMs may directly memorize the answers to multi-hop questions.
hops, indicating LLMs do not
We surprisingly find that except that ICE may bring marginal improvement to the pre-edit performance, the other knowledge editing techniques even mostly underperform pre-edit Portability Scores, showing another type of negative effect of knowledge editing and LLMs may not really reason with the edited knowledge in multi-hop questions for most knowledge editing methods. Comparing single-hop and multi-hop performance, we observe sharp decrease for all the editing methods, which further underscores the challenges of answering multi-hop questions with edited knowledge.
Insight 3: (1) LLMS may memorize answers rather than reason based single-hop knowledge for multi-hop questions; (2) Editing methods marginally improve or degrade pre-edit Portability Scores, implying LLMs may not really reason with edited knowledge multi-hop questions.

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Figure 6: Locality Scores of Knowledge Editing Methods. Locality Scores (%) are measured by the unchanging rate on Locality Evaluation Questions after applying knowledge editing methods on LLMs. A higher Locality Score indicates that there is higher percentage of LLMs' answers to the unrelated questions keeping the same and a less side effect on general knowledge LLMS. The "overall" refers to the Locality Score (%) on the whole HalluEditBench embracing 9 domains for different methods. The Locality Score on each domain also reported in the figure.
3.4 FACET 4: LOCALITY
Figure 6 shows the Locality Scores of different editing techniques in each domain and the whole HalluEditBench, reflecting the side effect of knowledge editing on unrelated knowledge encoded in LLMs. Based on the overall Locality Scores, methods except FT-M and ICE is unsatisfactory. In particular, the overall Locality Scores for all editing techniques except FT-M and ICE Llama3-8B and Mistral-v0.3-78 are below 40%. suggesting a high undesired impact on L.MMs' answers to unrelated factual questions, though FT-M achieves overall score of around 80% Mistral-v0.3-7B and ICE gains 60% on Llama3-8B.
observe that the performance of all editing
Furthermore, we notice that domains and LLMs have high impact on the Locality Scores of knowledge editing methods. For example, the Locality Score for ICE in the "places" domain in Llama3-8B is near 80%, while the performance drops to only about 50% in the "art" domain for the same LLM. Although FT-L obtains Locality Score around 60% the "business" domain on Llama2-7B, its performance in the same domain on Mistral-v0.3-75 is almost 0%.
Due the impact of LLMs, we observe that the rankings by Locality Scores for editing techniques on different LLMs are highly distinct. For example, the Locality ranking on Llama2-7B is GRACE MEMIT < ROME FT-L < ICE < LoRA < FT-M. However, the ranking changes to FT-L < LoRA < MEMIT < ROME < GRACE < ICE < FT-M on Mistral-v0.3-7B. Comparing Figure 3 with Figure 6, we find there no noticeable correlation between Efficacy and Locality for different editing techniques. FT-M achieves relatively high Locality Scores despite its low Efficacy Scores.
Insight 4: (1) Locality Scores of editing methods except FT-M and ICE unsatisfactory; (2) Domains and LLMss have a high impact on Locality Scores, and Locality rankings are distinct across different LLMs; (3) Efficacy does not have noticeable correlation with Locality.
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Figure 7: Robustness Scores of Knowledge Editing Methods. Robustness Scores are calculated by the accuracy Robustness Evaluation Questions with M turns (M Scores as the Robustness Scores when M is O. The Robustness Scores on two domains "human" and "places" are reported in the figure. The results for more domains are given in Appendix E.3. The "overall" refers to the Robustness Score (%) on the whole HalluEditBench embracing 9 domains.
10). We regard Efficacy
3.5 FACET 5: ROBUSTNESS
proposed Robustness Scores (%) to evaluate the resistance edited knowledge against distractions in prompts. Initially (M 0), LLMs are assessed with Efficacy Evaluation Questions. Then (M 1 10), LLMs are sequentially prompted with Robutness Evaluation Questions, which are exemplified in Figure 1, for M turns. Robustness Scores calculated with the percentage of "Yes" in each round. higher Robustness Score indicates that there a larger percentage of LLMSs can resist external manipulations in the prompt and higher extent of robustness for the edited knowledge.
First, based on overall Robustness Scores, we observe that LLMs themselves have large impact on the robustness of edited knowledge. The turns increase on different LLMs. For example, all editing methods have a sharp drop when turns go up on Llama2-7B: showing low level of robustness. However, MEMIT, ROME on Llama3-88 and Mistral-v0.3 - maintain almost the same and relatively high performance turns increase, suggesting a comparatively high level of robustness for the edited knowledge.
editing method could show distinct trends as
Then, we notice that both ICE and GRACE have a low level of robustness though they outperform the other five editing techniques regarding Efficacy Scores, showing the potential weaknesses on robustness of ceaumtter-presevvnnn knowledge editing methods. However, parameter pordiyigr-modiffiing editing techniques do not necessarily have high robustness, which is exemplified by LoRA.
Insight 5: (1) LLMs have large impact the Robusmess of edited knowledge; (2) Parameter- preserving knowledge editing methods such as ICE and GRACE potentially have low Robustness.

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4 RELATED WORK
Knowledge editing techniques have attracted increasing attention for their efficiency advantages in addressing obsolete or hallucinated information in LLMs (Wang et al., 2023c; Zhang et al., 2024f). In general, the existing editing techniques can be categorized into four types including Locate-then- edit (Meng et al., 2022; 2023), Fine- Fine-tuning based (Gangadhar & Stratos, 2024; Zhu et al., 2020; Wang et al., 2024a), In-Context Editing (Zheng et al., 2023; Shi et al., 2024; Fei et al., 2024). and Memory-based (Wang et al., 2024d; Hartvigsen et al., 2024; Mitchell et al., 2022; Yu al., 2023). Recently, many benchmarks have been built to investigate the properties of knowledge editing from different perspectives (Rosati et al., 2024; Wu et al., 2023; Ge et al., 2024a; Ma et et., 2023; Wei et al., 2023; 2024a; Zhong et al., 2023; Lin et al., 2024; Huang et al., 2024c; Liu et al., 2024c; Akyürek et al., 2023; Li et al., 2024a;f; 2023b; Gu et al., 2024; Powell et al., 2024; Yang et al., 2025; Du. et al., 2025; Zhang al., 2024a). For example, Gu et al. (2024) proposed a benchmark to assess the side effect of 4 popular editing methods on 3 LLMs across general capacity tasks. Rosati et al. (2024) built a new evaluation protocol to measure the efficacy and impact of knowledge editing in long-form generation. Wei et al. (2024a) introduced a multilingual knowledge editing benchmark embracing five languages. However, considering the fundamental motivation of applying knowledge editing L.MMs, which is to correct hallucinations, there is pressing need to build a real-world hallucination dataset with rigorous verification and systematically analyze the performance different editing methods. Thus, we proposed HalluEditBench to fill in the gap and provided new insights to facilitate the progress the field of knowledge editing.
CONCLUSION
In this paper, we have built a new benchmark HalluEditBench to holistically assess diverse knowl- edge editing techniques in correcting real-world hallucinations. First, we meticulously construct massive and comprehensive hallucination dataset based on Wikidata with domains, 26 topics, and more than 6, 000 hallucinations. Then, we systematically investigate the performance of different knowledge editing methods from five perspectives including Efficacy, Generalization, Portability, Locality, and Robustness. Our findings reveal that previous benchmarks cannot reflect the true effectiveness of knowledge editing methods in correcting real-world hallucinations and current edit- ing methods mostly show limited performance across five dimensions. We also offer valuable and actionable insights to inspire future advancements in knowledge editing for large language models.
ACKNOWLEDGMENTS
This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number I7STQAC0000107--04 NSF awards (SaTC-2241068, IIS-2506643, and POSE- 2346158), a Cisco Research Award, and a Microsoft Accelerate Foundation Models Research Award. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security and the National Science Foundation.
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REFERENCES
Zhila Aghajari, Eric PS Baumer, and Dominic DiFranzo. Reviewing interventions to address misinformation: the need to expand our vision beyond an individualistic focus. Proceedings of the ACM Human-Computer Interaction, ([CSCWI):1334 2023.
Afra Feyza Akyürek, Eric Pan, Garry Kuwanto, and Derry Wijaya. Dune: Dataset for unified editing. ArXiv preprint, abs/23 1. 16087, 2023. URL hnttps://arkiie httt:p/:/aarx...oggabb////////// org/abs/23111 16087.
Joseph CokeCCo,mman, Ian Kennedy, Morgan Wack, Andrew Beers, Joseph Schafer, Emma Spiro, Kate Starbird, and Jevin D West. Combining interventions to reduce the spread of viral misinformation. Nature Human Behaviour, 6010):1372-138, 2022.
Alimohammad Beigi, Zhen Tan, Nivedh Mudiam, Canyu Chen, Kai Shu, and Huan Liu. Model attribution in machine-generater disinformation: domain generalization approach with super- vised contrastive learning. ArXiv preprint, 0bbs22407.21225 2024. URL https://arxiv.. abs/2407.2100 21264..
Baolong Bi, Shenghua Liu, Lingrui Mei, Yiwei Wang, Pengliang Ji, and Xueqi Cheng. Decod- ing by contrasting knowledge: Enhancing iims' confidence on edited facts. ArXiv preprint, abs/2405. 11613, 2024a. URL https: ttttpp://aaxiii org/ abs//405. 11613.
Baolong Bi, Shenghua Liu, Yiwei Wang. Lingrui Mei, Hongcheng Gao, Junfeng Fang, and Xueqi Cheng. Struedit: Structured outputs enable the fast and accurate knowledge editing for large language models. ArXiv preprint, hass/4009100333 2024b. URL https:///axxii org/ //g//bb//449 10132.
Baolong Bi, Shenghua Liu, Yiwei Wang, Lingrui Mei, Hongcheng Gao, Yilong Xu, and Xueqi Cheng. Adaptive token biaser: Knowledge editing via biasing key entities. arXiv preprint arXiv: 2406.12468, 2024c.
Yuchen Cai. Ding Cao, Rongxi Guo, Yaqin Wen, Guiquan Liu, and Enhong Chen. Editing knowledge representation of language lodel via rephrased prefix prompts. ArXiv preprint, 143324, 2024a. URL https://arxiv.cr orgoorggggoooo abs/2403. 14381.
Yuchen Cai, Ding Cao, Rongxi Guo, Yaqin Wen, Guiquan Liu, and Enhong Chen. Locating and mitigating gender bias in large language models. ArXiv preprint, abs/2403. 14409, 2024b. URL https://arxix orgg abs/2403 14409.
Canyu Chen and Kai Shu. Combating misinformation the age llms: Opportunities and challenges. Al Magazine, 2024a. doi: 10.1002/aaai. 12188. URL https: / http://oo..oooo org/ 1002/aaai. 12188.
Canyu Chen and Kai Shu. Can LL.M-genrraed misinformation be detected? In The Twelfth International Conference on Learning Representations, 2024b. URL https://openrevieet net/ forum?1d=:cc04mtkkT
Canyu Chen, Haoran Wang, Matthew Shapiro, Yunyu Xiao, Fei Wang, and Kai Shu. Combating health misinformation in social media: Characterization, detection, intervention, and open issues. ArXiv preprint, abs/221 11 1.00289, 2022. URL httpt: wttp./...gggbb//21111/// 05289.
Canyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, Xifeng Yan, William Yang Wang, Philip Torr, Dawn Song, and Kai Shu. Can editing 1lms inject harm? ArXiv preprint, abs/2407 202244 2024a. URL https://arxie wrg/abs/////eeoooooo.oooooo 2407. 20224.
Qizhou Chen, Chengyu Wang. Dakan Wang, Taolin Zhang, Wangyue Li, and Xiaofeng He, Lifelong knowledge editing for vision language models with low-rank mixture-of-expert. arXiv preprint arXiv:2411.15432 2024b.
Qizhou Chen, Taolin Zhang, Dongyang Li, Longtao Huang, Hui Xue, Chengyu Wang, and Xiaofeng He. Lifelong knowledge editing for 1lms with retrieval-augmenter continuous prompt learning. ArXiv preprint, Abs/2405.03279, 2024c. URL httpppp:///.xwoooo///b////////// 03279.
11

---

Published as a conference paper at ICLR 2025
Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, and Jun Zhao. Journey to the center of the knowledge neurons: Discoveries imguage-indepeneee knowledge neurons and degenerate knowledge neurons. In Proceedings of the AAAI Conference Artificial Intelligence, volume 38, pp. 17817-17825, 2024d.
Yuheng Chen, Pengfei Cao, Yubo Chen, Kang Liu, and Jun Zhao. Knowledge localization: Mission not accomplished? enter query localization! ArXiv preprint, abs/2405. 14.. 17. 2024e. URL 14117.
Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Ling, Yuxuan Zhai, Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, and Di Wang. Leveraging logical rules in knowledge editing: A cherry on the top. ArXiv preprint, 0tbp/400.155555 2024a. URL https: //tp:i/////vv/e/eeeeeeeeeeeeeeee www.oogg///////eooooooooooee///e 2405. 15452.
Keyuan Cheng, Gang Lin, Haoyang Fei, Lu Yu, Muhammad Asif Ali, Lijie Hu, Di Wang. al. Multi-hop question answering under temporal knowledge editing. ArXiv preprint, abs/2404.00492, 2404. 00492.
2024b. URL https: http://axxvvvor/oeee
Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. Evaluating the ripple effects of knowledge editing in language models. Transactions of the Association for Computational Linguistics, 12:283-298, 2024.
Jingcheng Deng, Zihao Wei, Liang Pang, Hanxing Ding, Huawei Shen, and Xueqi Cheng. Unke: Unstructured knowledge editing in large language models. ArXiv preprint, abs/2405. 15349, 2024. URL https://arxinn org/ abs/2405. 15349.
Yuntao Du., Kailin Jiang, Zhi Gao, Chenrui Shi, Zilong Zheng, Siyuan Qi, and Qing Li. MMKE- bench: A multimodal editing benchmark for diverse visual knowledge. In The Thirteenth Interna- tional Conference on Learning Representations, 2025. URL https:: wewpenrreeeeeeeeeoorrreee id=v8qABSeeKO.
Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Xiang Wang, Xiangnan He, and Tat-seng Chua. Alphaedit: Null-space constrained knowledge editing for language models. ArXiv preprint, abs/2410.02355, 2024. URL https: //arxiv orgg/bs///11 02355.
Weizhi Fei, Xueyan Niu, Guoqing Xie, Yanhua Zhang, Bo Bai, Lei Deng, and Wei Han. Re- trieval meets reasoning: Dynamic in-context editing for long-text understanding. ArXiv preprint, 1b33, 2024. URL https: / orgg/br////// 12331.
Javier Ferrando, Gabriele Sarti, Arianna Bisazza, and Marta R Costa-jussà. A primer on the inner workings of transformer-basee language models. ArXiv preprint, htss/2405.00008. 2024. URL
https://arxiv.orr wttp:////r///b//////0// http:/2220.000 00208.
Govind Gangadhar and Karl Stratos. Model editing by pure fine-tuning. ArXiv preprint, abs/2402.11078,, 2024. URL https://aaxiii http://aaxii...rr///////2//// argg 11078.
Huaizhi Ge, Frank Rudzicz, and Zining Zhu. How well can knowledge edit methods edit perplexing knowledge? ArXiv preprint, abs/2406. 11s22406.17233, 2024a. URL https://arxiv.org/abb///// 2406. 17253.
Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, and Yunyao Li. Time sensitive knowledge editing through efficient finetuning. ArXiv preprint, 045/24006004466, 2024b. URL
org/abs//a 240..2406.... 04496.
Keltin Grimes, Marco Christiani, David Shriver, and Marissa Connor. Concept-rot: Poisoning concepts in large language models with model editing. arXiv preprint 2uXiv:2412.13344 2024.
Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, and Xin Wang. Pokemqa: Programmable knowledge editing for multi-hop hopp question answering. ArXiv preprint. abs/2312.15194, 2023. URL https: /
2212. 121221517 15194.
Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen Hua Ling, Kai-Wei Chang, and Nanyun Peng. Model editing harms general abilities of large language models: Regularization to the rescue. ArXiv preprint, abs/2401 .47700, 2024. URL https: /awxxvv..vv/////////a//eaa////i www.wii..o../eeee org/abs/ /401 04700.
12

---

Published as a conference paper at ICLR 2025
Xiaojie Gu, Guangxu Chen, Shuliang Liu, Jungang Li, Aiwei Liu, Sicheng Tao, Junyan Zhang, and Xuming Hu. Editing large language models via adaptive gradient guidance. In AAAI 2025 Workshop on Preventing and Detecting LLM Misinformation (PDLM), 2025. URL https: // openreview.ner http://wwww.eeeoouumeiieeeeeeeee m
Akshat Gupta, Anurag Rao, and Gopala Anumanchipalli. Model editing at scale leads to gradual and catastrophic forgetting. ArXiv preprint, abs/2401.07453 2024. URL httpps//aaaae www.vv..rr//booo //gg////////
Tom Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. Aging with grace: Lifelong model editing with discrete key-value adaptors. Advances in Neural Information Processing Systems, 36, 2024.
Katrin Hartwig, Frederic Doell, and Christian Reuter. The landscape of user-centered misinformation interventions-a systematic literature review. ACM Computing Surveys, 56(11):1-36, 2024.
Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun. Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models.
Advances in Neural Information Processing Systems, 36, 2024a.
Peter Hase, Thomas Hofweber, Xiang Zhou, Elias Stengel-Eskin, and Mohit Bansal. Fundamental problems with model editing: How should rational belief revision work in Ilms? ArXiv preprint, abs/2406.11354 2024b. URL https://aaxivvogg//a///
19354.
Bing He, Mustaque Ahamad, and Srijan Kumar. Reinforcement learning-based counter- misinformation response generation: a case study of covid-19 vaccine misinformation. In Proceed- ings of the ACM Web Conference 2023, pp. 2698-228222 2709, 2023.
Jason Beeascher-Obermaie
Julia Persson, Esben Kran, Ioannis Konstas, and Fazl Barez. Detecting edit failures in large language models: An improved specificity benchmark. ArXiv preprint, abs/2305. 17553, 2023. URL https::tp///.m //axxiv////xv.ve///////e/ wttp:or///gbb//////ss 17553.
Cheng-Hsun Hsuch, Paul Kuo-Ming Huang,
Lin, Che-Wei Liao, Hung-Chich Fang. Chao- Wei Huang, and Yun-Nung Chen. Editing the mind of giants: An in-depth exploration of pitfalls of knowledge editing in large language models. ArXiv preprint, abs/2406.01436, 2024. URL https://arr //axxv herg/abs/2450 0atss/2400000 01436.
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. In The Tenth Interna- tional Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022. URL https: http////ppp//eeeeee/eeeeeee net www..neeeforrum.nddeeeeeeee
Wenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, and Zhiguo Wang. Propagation and pitfalls: Reasoning based assessment of knowledge editing through counterfactual tasks. ArXiv preprint, abs/2401.11588. 2024. URL https://arrxe wwtprriw...gg.rb////// org/abs/2401 17585.
Baixiang Huang, Canyu Chen, and Kai Shu. Authorship attribution in the era of 1lms: Problems, methodologies, and challenges. ArXiv preprint, 00040/00000009466, 2024a. URL https://arxit: vrg/abs/2408.. 2405//
Baixiang Huang, Canyu Chen, and Kai Shu. Can large language models identify authorship?, 2024b. URL https://arxii: org/ /b/////s///// 08213.
Han Huang, Haitian Zhong. Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. Kebench: A benchmark on knowledge editing for large vision- laniuag models. ArXiv preprint, abs/2403.07350, 2024c. URL https:://axxv..org////////
wtwpiii.org///baa obgg 07350.
Houcheng Jiang, Junfeng Fang, Tianyu Zhang, An Zhang, Ruipeng Wang, Tao Liang, and Xiang Wang. Neuron-level sequential editing for large language models. ArXiv preprint, abs/2410.04045,
2024a. URL / org/ abs/abbs/tt /2t/0.410.000 04045.
Houcheng Jiang, Junfeng Fang, Ningyu Zhang, Guojun Ma, Mingyang Wan, Xiang Wang. Xiangnan He, and Tat-seng Chua. Anyedit: Edit any knowledge encoded in language models. arXiv preprint arXiv:2502.00 05628, 2025.
13

---

Published as a conference paper at ICLR 2025
Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, et al. Learning to edit: Aligning Iims with knowledge editing. ArXiv preprint. abs/2402.11900 2024b. URL https: //axxiv org. aas///////// /ttp//////2/.// 1985.
Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, and Bozhong Tian. Mike: A new benchmark for fine fine-grained multimodal entity knowledge editing. ArXiv preprint, abs/2402. 14835, 2024a. URL htttps://araiii wrw.lv.oooggooooo orgg/bss//42// 14835.
Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, and Wai Lam. Consecutive model editing with batch alongside hook layers. ArXiv preprint, abs/2403.05330, 2024b. URL https: //arxiv. org/ abs//40. 2403. .5330.
Xiaopeng Li, Shasha Li, Shezheng Song, Jing Yang, Jun Ma, and Jie Yu. Pmet: Precise model editing in transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 18564-18572, 2024c.
Xiaopeng Li, Shangwen Wang. Shezheng Song, Bin Ji, Huijun Liu, Shasha Li, Jun Ma, and Jie Yu. Identifying knowledge editing types in large language models. arXiv preprint arXiv:2409. 19663, 2024d.
Yanhong Li, Chunling Fan, Mingqing Huang, and Chengming Li. Learning from mistakes: comprehensive review of knowledge editing for large language models. In 2024 IEEE International Conference on Smart Internet of Things (SmartoT)), pp. 563-569. IEEE, 2024e.
Zherui Li, Houcheng Jiang, Hao Chen, Baolong Bi, Zhenhong Zhou, Fei Sun, Junfeng Fang. and Xiang Wang. Reinforced lifelong editing for language models. arXiv preprint arXiv:2502.07759 2025.
Zhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen, and Huajun Chen. Unveiling the pitfalls of knowledge editing for large language models. ArXiv preprint, abs/2310.02129, 2023a. URL https://arxxv.orrr www.:.oog//bbooe 2b5/ 02129.
Zhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen, and Huajun Chen. Unveiling the pitfalls of knowledge editing for large language models. In The Twelfth International Conference on Learning Representations, 2024f. URL https: /tppn://////eeeeeee/ee net/ httt//frmmmiicoo f
Zichao Li, Ines Arous, Siva Reddy, and Jackie Chi Kit Cheung. Evaluating dependencies in fact editing for language models: Specificity and implication awareness. In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 7623-7636, 2023b.
Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang. Wenpeng Yin, and Lifu Huang. Navigating the dual facets: comprehensive evaluation of sequential memory editing in large language models. ArXiv preprint, abs/2402. 1122, 2024. URL https: //arxiv.org/abs/// 2tt..////22...112 11122.
Guofan Liu, Jinghao Zhang, Qiang Liu, Junfci Wu, Shu Wu, and Liang Wang. Uni-modal event- agnostic knowledge distillation for multimodal fake news detection. IEEE Transactions Knowledge and Data Engineering, 2024a.
Jiateng Liu, Pengfei Yu, Yuji Zhang, Sha Li, Zixuan Zhang, and Heng Ji. Evedit: Event-based knowledge editing with deductive editing boundaries. ArXiv preprint, 1ttp22422.13244. 2024b. URL htwps:ttp///t //axiiv////iii///////// org/ 2402. 11324.
Tianci Liu, Zihan Dong. Linjun Zhang, Haoyu Wang, and Jing Gao. Mitigating heterogeneous token overfitting in Ilm knowledge editing. arXiv preprint 2rXiv:2502.00602, 2025.
Zeyu Leo Liu, Shrey Pandit, Xi Ye, Eunsol Choi, and Greg Durrett. Codeupdatearena: Benchmarking knowledge editing on api updates. ArXiv preprint, 025/2407.06249, 2024c. URL https://arxiv.. org/abs/2er 2407 0402//6277766..
Yifan Lu, Yigeng Zhou, Jing Li. Yequan Wang, Xuebo Liu, Daojing He, Fangming Liu, and Min Zhang. Knowledge editing with dynamic knowledge graphs for multi-hop question answering. arXiv preprint 2rXiv:24/2.13782 2024.
14

---

Published as a conference paper at ICLR 2025
Jun-Yo Yu Ma, fia-Chen Gu, Zhen-Hua Ling, Quan Liu, and Cong Liu. Untying the reversal curse via bidirectional language model editing. ArXiv preprint, abs/2310.10322, 2023. URL https: //aaxivv.orgg 2310 10322.
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems, 35:17359- 17372, 2022.
Kevin Meng, Arnab Sen Sharma, Alex J Andonian, Yonatan Belinkov, and David Bau. Mass-editing memory in a transformer. The Eleventh International Conference on Learning Representations, 2023. URL
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, and Chelsea Finn. Memory- based model editing at scale. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato (eds.), International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pp. 15817-15831. PMLR, 2022. URL nttpss:/pproceddings. mlr. press/v162/ mi mitchel122a, html.
Qiong Nan, Qiang Sheng. Juan Cao. Yongchun Zhu, Danding Wang, Guang Yang, Jintao Li, and Kai Shu. Exploiting user comments for early detection fake news prior to users' commenting. ArXiv preprint, abs/2310.1042)) 2023. URL https: httpa/////aiiooor/a/////333 org/abs/2310.. 10429.
Qiong Nan, Qiang Sheng, Juan Cao, Beizhe Hu, Danding Wang, and Jintao Li. Let silence speak: Enhancing fake news detection with generated comments from large language models. In Proceed- ings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 1732 1742, 2024.
Bo Ni, Zheyuan Liu, Leyao Wang, Yongjia Lei, Yuying Zhao, Xueqi Cheng, Qingkai Zeng, Luna Dong, Yinglong Xia, Krishnaram Kenthapadi, et al. Towards trustworthy retrieval augmented generation for large language models: A survey, arXiv preprint arXiv:2502.0687, 2025.
Jingeheng Niu, Andrew Liu, Zining Zhu, and Gerald Penn. What does the knowledge neuron thesis have to do with knowledge? ArXiv preprint, abs/2405.00421,, 2024. URL https:///////ii.orrr abs/2405. 02421.
Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo. Yixin Cao, Lei Hou, and Juanzi Li. Event-level knowledge editing. ArXiv preprint, abs/2402. 13093, 2024. URL
https://arxiv. www.xiii.orrggaaeeee www.orgg///ao..e ht////bs/2222... 13093.
Derek Powell, Walter Gerych, and Thomas Hartvigsen. Taxi: Evaluating categorical knowledge editing for language models. ArXiv preprint, abs/2404.150044 2024. URL https://axx/
abs/2404. 5004.
Siyuan Qi, Bangcheng Yang, Kailin Jiang, Xiaobo Wang, Jiaqi Li, Yifan Zhong, Yaodong Yang, and Zilong Zheng. In-context editing: Learning knowledge from self-induced distributions. ArXiv preprint, 155/22006.11994 2024. URL
hrgg/bs/24066 11194.
Domenic Rosati, Robie Gonzales, Jinkun Chen, Xuemin Yu, Melis Erkan, Yahya Kayani, Satya Deepika Chavatapalli, Frank Rudziez, and Hassan Sajjad. Long-form evaluation of model editing. ArXiv preprint, abs/2402.09394, 2024. URL https: //appvv.////v....////// www.eriii.oog/g/eeeeeeeeeeeeeeee hwwpg//bb/222....o 093994.
Amit Rozner, Barak Battash, Lior Wolf, and Ofir Lindenbaum. Knowledge editing in language models via adapted direct preference optimization. arXiv preprint arXiv: 2406.09920, 2024.
Arna Sen Sharma, David Atkinson, and David Bau. Locating and editing factual associations in mamba. ArXiv preprint, abs/2404.03646 2024a. URL https://arxi:: http:///wwwwwww..aaaba///ssa wttp/b//////////ooo
03646.
Mrinank Sharma, Meg Tong. Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Esin DURMUS, Zac Hattield-Dodds, Scott R Johnston, Shauna M Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez. Towards understanding sycophancy in language models. In The Twelfth International Conference on Learning Representations, 2024b. URL https: /ttp////ppeeeeee.///////e/ net for tvhaxkMKAn.
15

---

Published as a conference paper at ICLR 2025
Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, and Ninghao Liu. Retrieval-cnbanced knowledge editing in language models for multi-hop question answering. In Proceedings of the 33rd ACM International Conference Information and Knowledge Manage- ment, pp. 2056-2066, 2024.
Yucheng Shi, Tianze Yang, Canyu Chen, Quanzheng Li, Tianming Liu, Xiang Li, and Ninghao Liu. Searchrag: Can search engines be helpful for Ilm-based medical question answering? arXiv preprint WrXiv:2502.132333 2025.
Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. Fake news detection social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1):22-33 36, 2017.
Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Canyu Chen, Hal Daumé III, Jesse Dodge, Isabella Duan, al. Evaluating the social impact of generative ai systems in systems and society. ArXiv preprint, abs/2306.05949, 2023, URL httpp://arxa org/abs/2306. 05949.
SM Tonmoy, SM Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das. A comprehensive survey of hallucination mitigation techniques in large language models. ArXiv preprint, abs/2400.0111 2024. URL http:///aaxxv..o/ab.ssss
.03133.
Rheeya Uppaal, Apratim De, Yiting He, Yiquao Zhong, and Junjie Hu. Detox: Toxic subspace projection for model editing. ArXiv preprint, abs/2405.13967, 2024. URL https:t/axxiv..ot org/ abs/2405. 13967.
Bertie Vidgen, Adarsh Agrawal, Ahmed M Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj. Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, et al. Introducing v0. 5 of the ai safety benchmark from micommons. ArXiv preprint, abs/2404. 12241, 2024. URL attps::/arxxt http:////arxi..o//b/////e// abs///4//240 12241.
Haoran Wang, Yingtong Dou, Canyu Chen, Lichao Sun, Philip S Yu, and Kai Shu. Attacking fake news detectors via manipulating news social engagement. In Proceedings of the ACM Web Conference 2023, pp. 1978-3988 2023a.
Haoyu Wang, Tianci Liu, Tuo Zhao, and Jing Gao. Roselora: Row and column-wise sparse low-rank adaptation pre-trained language model for knowledge editing and fine-tuning. ArXiv preprint. abs/2406.10777 2024a. URL httpp::/aaat //tp:///////vv///a//a org /40p. 10777.
Jiaan Wang. Yunlong Liang, Zengkui Sun, Yuxuan Cao, and Jiarong Xu. Cross-lingual knowledge editing in large language models. ArXiv preprint, 0os//309.08952, 2023b. URL https://arri:
htggabb/2339 08952..
Mengru Wang, Yunzhi Yao. Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, et al. Knowledge mechanisms in large language models: A survey and perspective. ArXiv preprint, abs/2407. 2024b. URL https::/aaxii: org/abgg 2407.15017
Mengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, and Huajun Chen. Detoxifying large language models via knowledge editing. ArXiv preprint, abs/2403.14477 14472, 2024c. URL
14472.
Peng Wang, Zexi Li, Ningyu Zhang, Ziwen Xu, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. Wise: Rethinking the knowledge memory for lifelong model editing of large language models. ArXiv preprint, 22/.22/45..466 2024d. URL http://awxivv.oo/a////////////// org/abs/24055. 14768.
Renzhi Wang and Piji Li. Lemoe: Advanced mixture of experts adaptor for lifelong model editing of large language models. ArXiv preprint, abs/2406.20030 2024a. URL hnttps://arxxt wwwpxxivv.rrr//ooooooooooe org. / 2406. 20030.
Renzhi Wang and Piji Li. Semantic are beacons: A semantic perspective for unveiling parameter- efficient fine-tuning in knowledge learning. ArXiv preprint, 0tsx22405.12999 2024b. URL https: //arxiv.orgi org/ abs /205..2255. 18292.
16

---

Published as a conference paper at ICLR 2025
Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, et al. Knowledge editing for large language models: A survey. ArXiv preprint, abs/2310.16211 2023c. URL https: waarxiv.org/ab///222/
16218.
Wenxuan Wang, Juluan Shi, Zhaopeng Tu, Youliang Yuan, Jen-tse Huang, Wenxiang Jiao, and Michael R Lyu. The earth is flat? unveiling factual enrors in large language models. ArXiv preprint, abs/2401.00761, 2024e. URL https: //tppii///xxv..oo/ wwtpxxivvorr///ooae orgg/bs//2/11111. 20010.
Xiaohan Wang, Shengyu Mao, Ningyu Zhang, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, and Huajun Chen. Editing conceptual knowledge for large language models. ArXiv preprint, 0tbs/2200.00255 2024f. URL https: //arxiv...ivv///aa wwwpaaawwwwa..ooaaaaooaaaaerweee htrgg/b///4/// 06259.
Yiwei Wang, Muhao Chen, Nanyun Peng, and Kai-Wei Chang. Deepedit: Knowledge editing as decoding with constraints. ArXiv preprint, abs/2401.10471, 2024g. URL https:///axiiv.orrg
abs/2401. 10471.
Yifan Wei, Xiaoyan Yu, Huanhuan Ma, Fangyu Lei, Yixuan Weng, Ran Song, and Kang Liu. Assess- ing knowledge editing language models via relation perspective. ArXiv preprint, 10.903,, 2023. URL https: //axxiv http:///awwiv..ooa/////////a 2311. 09053.
Zihao Wei, Jingcheng Deng, Liang Pang, Hanxing Ding, Huawei Shen, and Xueqi Cheng. Mlake: Mul- tilingual knowledge editing benchmark for large language models. ArXiv preprint, abs/2404.04990, 2024a. URL https: //arxiv org/ ab4/2404. 04990.
Zihao Wei, Liang Pang, Hanxing Ding, Jingcheng Deng, Huawei Shen, and Xueqi Cheng. Stable knowledge editing in large language models. ArXiv preprint, abs/2402.13048, 2024b. URL
https://arxiv.org/
13048.
Suhang Wu, Minlong Peng. Yue Chen, Jinsong Su, and Mingming Sun. Eva-kellm: new benchmark for evaluating knowledge editing of Ilms. ArXiv preprint, (bss22008.09954 2023. URL https:
www.iii.orggbb////e// 09954..
Xiaobao Wu, Liangming Pan, William Yang Wang, and Anh Tuan Luu. Updating language models with unstructured facts: Towards practical knowledge editing. ArXiv preprint, 18892402.18809, 2024. URL https::t////// /aaxxiv...ivv///aa http:/aaxxv...oobabb/a/// org/abs/ 2402/ 18909.
Yuchen Wu, Liang Ding, Li Shen, and Dacheng Tao. Edit once, update everywhere: A simple framework for cross-lingual knowledge synchronization llms. arXiv preprint arXiv:2502. 14645, 2025.
Jiakuan Xie, Pengfei Cao, Yuheng Chen, Yubo Chen, Kang Liu, and Jun Zhao. Memla: Enhancing multilingual knowledge editing with neuron- masked low-rank adaptation. arXiv preprint arXiv: 2406. / /1666 2024.
Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, and Enhong Chen. Editing factual knowledge and explanatory ability of medical large language models. ArXiv preprint, 0bs/2402.18099, 2024. URL https://arxts org/abs/2402. 2ttis//222.11800.0000 18099.
Jianhao Yan, Futing Wang, Yafu Li, and Yue Zhang. Potential and challenges of model editing for social debiasing. ArXiv preprint, 12.22402..1442., 2024. URL https://arxivv wwtprxiv.orggab////// 13462.
Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, and Xueqi Cheng. The butterfly effect of model editing: Few edits can trigger large language models collapse. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.). Findings of the Association for Computational Linguisties: Bangkok, Thailand, August 2024a. Association for Computational Linguistics. doi: 10. 18653/v1/2024 windinggs--al..33 URL https: /awpaathoaaaaltyyyooo/ooo org/ 2024. findings-acl. 322/.
ACL 2024, pp.
Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Du Su, Dawei Yin, and Huawei Shen. The fall of rome: Understanding the collapse of 1lms in model editing. In Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 4079-4087, 2024b.
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---

Published as a conference paper at ICLR 2025
Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Qi Cao, Dawei Yin, Huawei Shen, and Xueqi Cheng. The mirage of model editing: Revisiting evaluation in the wild. arXiv preprint arXiv:2502.1117) 2025.
Yunzhi Yao, Peng Wang. Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. Editing large language models: Problems, methods, and opportunities. ArXiv preprint, abs/2305.13172. 2023. URL https: //axxiv. wtwg/ba/////////// 13172.
Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, and Huajun Chen. Knowledge circuits in pretrained transformers. ArXiv preprint, abs/2405.17969, 2024. URL
https://arxis /awxiv../iivvvvvaa///// wwweriii...ogbboo.eeeeeee wwwp/bbg////aeeeeeeoeee 17969.
Xunjian Yin, Jin Jiang, Liming Yang, and Xiaojun Wan. History matters: Temporal knowledge editing in large language model. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19413-19421, 2024.
Paul Youssef, Zhixue Zhao, Christin Seifert, and Jörg Schlötterer. Has this fact been edited? detecting knowledge edits in language models. arXiv preprint arXiv:2405.02765, 2024.
Paul Youssef, Zhixue Zhao, Daniel Braun, Jörg Schlötterer, and Christin Seifert. Position: Editing large language models poses serious safety risks. arXiv preprint arXiv:2502.02958. 2025.
Lang Yu, Qin Chen, Jie Zhou, and Liang He. Melo: Enhancing model editing with neuron-indexed dynamic lora. ArXiv preprint, abs/2312.11799 2023. URL 11795.
Zhenrui Yue, Huimin Zeng, Yimeng Lu, Lanyu Shang, Yang Zhang, and Dong Wang. Evidence- driven retrieval augmented response generation for online misinformation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguisties: Human Language Technologies (Volume 1: Long Papers), pp. 5628-5643, 2024.
Zhen Zeng, Leijiang Gu, Xun Yang. Zhangling Duan, Zenglin Shi, and Meng Wang. Visual- oriented fine-gran tine-grained knowledge editing for multimodal large language models. arXiv preprint M.Xiv:2411.127911 2024.
Binchi Zhang, Zhengzhang Chen, Zaiyi Zheng, Jundong Li, and Haifeng Chen. Resolving editing- unlearning conflicts: knowledge codebook framework for large language model updating. arXiv preprint 2rXiv:5502.00058. 2025a.
Junzhe Zhang, Huixuan Zhang, Xunjian Yin, Baizhou Huang, Xu Zhang, Xinyu Hu, and Xiaojun Wan. Mc-mke: fine-grained multimodal knowledge editing benchmark emphasizing modality consistency. arXiv preprini arXiv:2406. 13219, 2024a.
Mengqi Zhang, Bowen Fang, Qiang Liu, Pengjie Ren, Shu Wu, Zhumin Chen, and Liang Wang. Enhancing multi-hop reasoning through knowledge erasure large language model editing. ArXiv preprint, abs/2408. 12456, 2024b. URL https://arxiv.cr http://aaaiiioo....aaooooo abss/ 12456.
Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, and Zhumin Chen. Knowledge graph enhanced large language model editing. ArXiv preprint, abs/2402. 2024c. URL 13593.
Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, and Zhumin Chen. Uncovering overfitting large language model editing. ArXiv preprint, abs/2410.07819, 2024d. URL. 07819.
bttts://arxiv.orrr/bb//222
Ningyu Zhang, Zekun Xi, Yujie Luo, Peng Wang. Bozhong Tian, Yunzhi Yao, Jintian Zhang, Shumin Deng, Mengshu Sun, Lei Liang, et al. Oneedit: A neural-symbolis collaboratively knowledge editing system. ArXiv preprint, abs/2409.074977 2024e. URL http::::///axxxxor/////a// 07497.
Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, et al. A comprehensive study of knowledge editing for large language models. ArXiv preprint, abs/2401.01286 2024f. URL abs/2401. 01286.
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Published as a conference paper at ICLR 2025
Shaolei Zhang, Tian Yu, and Yang Feng. Truthx: Alleviating hallucinations by editing large language models in truthful space. ArXiv preprint, abs/2402.17811, 2024g. URL https:://arxiii /ttp::////xvvvooo///oe abs/2402.17811 17811.
Tianyu Zhang, Junfeng Fang, Houcheng Jiang, Baolong Bi, Xiang Wang, and Xiangnan He. Explain- able and efficient editing for large language models. In THE WEB CONFERENCE 2025, 2025b.
URL hwwph:tts//.. hopps://opents /ttp:////peeeeeeeee//eeeeee www..e/orrmmiiiii/11f......
Yihao Zhang, Zeming Wei, Jun Sun, and Meng Sun. Adversarial representation engineering: general model editing framework for large language models. Advances in Neural Information Processing Systems, 37:126243-126264 2025c.
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. Siren's is song in the ai ocean: A survey on hallucination in large language models. ArXiv preprint, abs/2309.01219, 2023. URL https: //arxii wwwiii.orrggaae orgg/bs/23099 01219.
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang. Junjie Zhang. Zican Dong, Yifan Du Chen Yang. Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian- Yun Nie, and Ji-Rong Wen. A survey of large language models. ArXiv preprint, abs/2303. 18223,, 2023. URL https://arxiv. htwp:iiwwwrg/////////e/a 2303. 18223.
Zongkai Zhao, Guozeng Xu, Xiuhua Li, Kaiwen Wei, and Jiang Zhong. Fleke: Federated locate-then- edit knowledge editing. arXiv preprint arXiv:2502. 15677, 2025.
Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang, Can we edit factual knowledge by in-context learning? In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Proceedings of the 2023 Conference on Empirical Methods Natural Language Processing. pp. 4862-4876, Singapore, 2023. Association for Computational Linguistics. doi: 10. 18653v13 2023.emnlp-main.294 URL https://acllattholpgg. 002/2023.0.. wttp:r//.......mmmmma...... emnlp-main.22 296.
Zexuan Zhong, Zhengxuan Wu, Christopher Manning, Christopher Potts, and Danqi Chen. Mquake: Assessing knowledge editing in language models via multi-hop questions. ArXiv preprint, habs/2305.14795 2023. URL http:://axivv.oro 220aabs//00.... 14795.
Yujia Zhou, Yan Liu, Xiaoxi Li, Jiajie Jin, Hongjin Qian, Zheng Liu, Chaozhuo Li, Zhicheng Dou, Tsung-Yi Yi Ho, and Philip S Yu. Trustworthiness in retrieval-augmeented generation systems: A survey. arXiv preprint arXiv:2409.10100 2024.
Chen Zhu Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix Yu, and Sanjiv Kumar. Modifying memories in transformer models. ArXiv preprint, abs/2012.00363, 2020. URL https://arr //axxvv.//iiv../// wttg/aas/202..0000000
Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Representation engineering: A top-down approach ai transparency. ArXiv preprint, abs/2310.01405 2023. URL https: // www.iv.org/abs//220......... 230.//2211...... 01405.
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Content of Appendix
A Reproducibility Statement
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Details of the Benchmarked Knowledge Editing Techniques
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A More Detailed Related Work
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D Impact Statement
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More Experiment Results
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E.1 Generalization Scores of Knowledge Editing Methods on Each Domain
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E,2 Portability Scores of Knowledge Editing Methods on More Domains
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E.3 Robustness Scores of Knowledge Editing Methods More Domains
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Examples of HalluEditBench
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A REPRODUCIBILITY STATEMENT
We conduct the experiments on NVIDIA RTX A6000 GPUs. The decoding temperatures are 0 to ensure the reproducibility. The model checkpoints are downloaded from https:// co/. The specific download links are as follows:
Llama2-7B:
Llama3-8B:
-ns-Innt
Mistral-v0.3-7B. https://hugginggeeett
We adopt GPT-4o with the prompt below to generate Generalization and Locality evaluation questions:
Given a fact triplet (subject, relation, object), a question asking for the object, and a wrong answer, the correct answer to the question should be the object in the triplet.
Generate the following types questions:
1. Paraphrascd question: Create a paraphrased version of the original question. The correct answer
should still be the object from the triplct.
2. Multiple choices: Generate four answer options for the original question in the following order: the
correct object from the triplct, the given wrong answer, and two additional distractors.
3. Yes question: Rewrite the original question as yes/no question by explicitly including the object from the triplet, ensuring that the correct answer is "Yes."
4. No question: Rewrite the original question as yes/no question by including the provided wrong answer, so that the correct answer to this question is "No."
5. Locality question: Generate question about well-known attribute related to the subject from the triplet. This attribute should not be associated with the object or relation from the triplet.
6. Reversed relation question: Generate question by swapping the subject and object from the original question. The answer should now be the subject from the triplet.
Output the result JSON format with the following keys: "paraphrased question". "multiple_choices" choices". ayes_question"s "no question". "locality "ursstuuees"""""" and "reversed relation question."
We adopt GPT-4o with the following prompt to generate evaluation questions in Portability aspect.
Given subject, a relation, a I-hop question, and its answer, create 2-hop, 3-hop. 4-hop. 5-hop, and 6-hop questions, along with their correct answers.
Always use the provided subject and relation create multi-hop questions and include the preceding question in the subsequent question (for example, include the 2-hop question in 3-hop question, include the 3-hop question in 4-hop question).
DO NOT include the correct answer any previous multi-hop question in subscquent ones (for example, do include the correct answer the 2-hop question the 3-hop or 4-hop questions).
Ensure that the answers for all multi-hop questions are accurate, and do not use 'N/A' as an answer. You must include the given subject and relation in all of the 2-hop, 3-hop, 4-hop, 5-hop, and 6-hop questions. Output in JSON format. An example provided below:
Example input:
subject: Amazon, relation: founder
Ihop_ quusstuustio:: Who is the Amazon founder? lhop_ hnow_answer: Jeff Bezos
Example output:
"2hop_question": "Who is the spouse of the Amazon founder?". "2hop_answer" "MacKenzie
Scott".
:uiop_question": "Which university did the spouse of the Amazon founder attend for their under- graduate studies?" schop_answert "shop_question" "In which city is university that the spouse the Amazon founder attended
"Princeton University", located?". "#hop_answer": "Princeton".
"Shop_question" "In which state is the city located where the university that the spouse of the Amazon founder attended situated?". "Shop_amse -nowerr"""" "New Jersey". "6hop q "In which country is the state located where the city is situated that contains the university the spouse of the Amazon founder attended?"" "6hop answer": "United States",
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B DETAILS OF THE BENCHMARKED KNOWLEDGE EDITING TECHNIQUES
FT-L (Zhu et al., 2020; Meng et al., 2022) Constrained Fine-Tuning (FT-L) is a targeted approach to fine-tuning that focuses on adjusting a specific layer within model's red-fforwrr network (FFN). Guided by causal tracing results from ROME, FT-L modifies the layer most associated with the desired changes. The goal of FT-L to fine-tune the model by maximizing the likelihood of the target sequence, particularly focusing on the prediction of the last token, ensuring that the model adapts to modified facts without affecting its broader performance. To achieve this, explicit parameter- norm constraints applicd to the weights, ensuring minimal interference with unmodified facts and preserving the integrity of the model's original knowledge.
FT-M (Zhang et al., 20241) In contrast to FT-L, which fine-tunes by maximizing the probability of all tokens in the target sequence based on the last token's 's prediction, Sine-Tuning with Masking (FT-M) refines this approach to align more closely with the traditional fine-tuning objective. FT-M also targets the same FFN layer identified by causal tracing but employs a masked training strategy. Specifically, it uses cross-entropy loss on the target answer while masking out the original text, ensuring that the model is trained directly on the relevant target content. This approach mitigates potential deviations from the original fine-tuning objective and provides a more precise adjustment of the model's weights with minimal disruption to unrelated model behavior.
LoRA (Hu et al., 2022) Low-Rank Adaptation (LoRA) is a parameter-efffiient fine-tuning method that enhances training efficiency by introducing trainable rank decomposition matrices into Transformer layers. Rather than updating the original model parameters directly, LoRA focuses on training expansion and reduction matrices with low intrinsic rank, which allows for significant dimensionality reduction and thus faster training. Specifically, LoRA freezes the pretrained model weights and optimizes rank decomposition matrices to indirectly adapt dense layers without altering the original parameters. This approach greatly reduces the number of trainable parameters needed for downstream tasks, enabling more efficient training and lowering hardware requirements.
ROME (Meng et al., 2022) Rank- Model Editing (ROME) a Loocate-then-Edee technique de- signed to modify factual associations within transformer models. ROME localizes these associations along three key dimensions: (1) the MLP module parameters, (2) within a range of middle layers, and (3) specifically during the processing of the last token of the subject. It employs causal intervention to trace the causal effects of hidden state activations, identifying the specific modules that mediate the recall of factual information. Once these decisive MLP modules are localized, ROME makes small, targeted rank-one changes to the parameters of a single MLP module, effectively altering individual factual associations while minimizing disruption to the overall model behavior. This precise parameter adjustment enables direct updates to the model's s factual knowledge.
MEMIT (Meng et al., 2023) Mass Editing Memory in Transformer (MEMIT) builds upon ROME to generalize the editing of feedforward networks (FFNs) in pre-trained transformer models for mass knowledge updates. While ROME focuses on localizing and modifying factual associations within single layers, MEMIT extends this strategy to perform mass edits across a range of critical layers. MEMIT uses causal tracing identify MLP layers that act as mediators of factual recall, similarly to ROME, but scales the process to enable the simultaneous insertion of thousands of new memories. By explicitly calculating parameter updates. MEMIT targets these critical layers and updates them efficiently, offering a scalable multi-layer update algorithm that enhances and expands upon ROME's capability to modify knowledge across many memories concurrently, achieving orders of magnitude greater scalability.
ICE (Zheng et al., 2023) In-Context Knowledge Editing (IKE) leverages in-context learning (ICL) to modify model outputs without altering the model's 's parameters. This approach reduces computational overhead and avoids potential side effects from parameter updates, offering more efficient and safer way to modify knowledge in large language models. IKE enhances interpretability, providing a human-understandaar three types of demonstrationsscoopy update, and retain-that guide the model in producing reliable fact editing through the use of a demonstration store. This store, built from training examples, allows the model to retrieve the most relevant demonstrations to inform its responses, improving accuracy in modifying specific factual outputs. In In-Context Editing (ICE) is a simple baseline variant of IKE, which directly uses the new fact as context without additional demonstrations.
method for calibrating model behaviors. achieves this by constructing
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GRACE (Hartvigsen al., 2024) GRACE is knowledge editing method designed to enable thousands of sequential edits without the pitfalls of overfitting or loss of previously learned knowledge, which are common in conventional knowledge editing approaches. GRACE introduces adaptor to a chosen layer of model, allowing for layer-to-layer transformation adjustments without altering the model's original weights. This adaptor caches embeddings corresponding input errors and learns values that map to the desired model outputs, effectively functioning as a codebook where edits are stored. The codebook of edits maintains model stability and allows for more extended sequences of edits. GRACE includes a deferral mechanism that decides whether to use the codebook for given input, enabling the model to dynamically search and replace hidden states based on stored knowledge. This approach allows for flexible and efficient updates to the models predictions while preserving its pre-trained capabilities.
C A MORE DETAILED RELATED WORK
Knowledge Editing has been adopted as one of the mainstream paradigms to address the hallucinations in LLMs efficiently (Chen & Shu, 2024a; Tonmoy et al., 2024; Li et al., 2024e). Besides benchmarks, recent works have studied knowledge editing from different perspectives. The first line of works aims to probe into the relationship between localization and editing and gain a deeper understanding of the working mechanisms of different techniques (Wang et al., 2024b; Niu et al., 2024; Hase et al., 2024a;b; Ferrando et al., 2024; Gupta et al., 2024; Chen et al., 2024e;d; Zou et al., 2023; Yao et al., 2024; Wu et al., 2025). For example, Hase et al. (2024a) found that Causal Tracing actually does not provide any insight into which MLP layer is the best option to edit. The second line of works intends to enhance the performance and applicability of knowledge editing in specific scenarios (Rozner et al., 2024; Jiang et al., 2024a;b; Zhang et al., 20::d;;;e;b;;;; 2025a;b; Wu et al., 2024; Qi et al., 2024; Sharma et al., 2024a; Li et al., 2024c;b; Fang et al., 2024; Wang & Li, 2024a;b; Wang et al., 2024g:fzd; 2023b; Cheng et al., 2024b;a; Xie et al., 2024; Bi et al., 2024c;b;a; Chen et al., 2024c;b; Wei et al., 2024b; Fei et al., 2024; Xu et al., 2024; Gu et al., 2023; Yin et al., 2024; Cai al., 2024a; Liu et al., 2024b; 2025; Ge et al., 2024b; Deng et al., 2024; Peng et al., 2024; Zhao et al., 2025; Jiang et al., 2025; Li et al., 2025; Lu et al., 2024; Zeng et al., 2024; Gu al., 2025). For example, Ma et al. (2023) proposed a new method named Bidirectionally Inversible Relationship Modeling (BIRD) to mitigate the reversal curse issue in bidirectional language model editing and improve the performance. The third line of works investigates the side effect of knowledge editing techniques (Hsueh et al., 2024; Gu et al., 2024; Seelsshhr--bbermiiee et al., 2023: Hua al., 2024: Yang et al., 2024a;b; Li et al., 2023a; Cohen et al., 2024). For example, Yang et al. (2024a) discovered that even one single edit could cause significant performance degradation in mainstream benchmarks. The fourth line of works explores the potential misuse risks of knowledge editing or its applications beyond correcting hallucinations (Chen et al., 2024a; Uppaal et al., 2024; Wang et al., 2024c; Cai et al., 2024b; Yan et al., 2024; Zhang et al., 2025c: Grimes et al., 2024; Li et al., 2024d; Youssef et al., 2024; 2025). For example, Chen et al. (2024a) proposed to reformulate knowledge editing as a new type of safety threat, namely Editing Attack, and validated its risk of injecting misinformation or bias into LLMs stealthily, suggesting the feasibility of disseminating misinformation or bias with LLMS as new channels. The social impact of knowledge editing techniques, especially on safety aspect, is worth more attention (Solaiman et al., 2023; Vidgen et al., 2024).
D IMPACT STATEMENT
Misinformation is longstanding threat for online safety and public trust (Chen et al., 2022; Wang et al., 2023a). The conventional countermeasures include detection (Shu al., 2017; Nan et al., 2024; 2023; Liu et al., 2024a), intervention (Bak-Coleman et al., 2022; Aghajari et al., 2023; Hartwig et al., 2024; Yue et al., 2024; He et al., 2023) and attribution (Huang et al., 2024a;b; Beigi et 2024). Hallucinations, which could be defined the non-factual information unintentionally generated by LLMs when used by normal users (Chen & Shu, 2024a:b), have become an new type of misinformation and may cause severe information pollution to the online space. Besides methods such Generation (Shi et al., 2025; Ni et al., 2025; Zhou et al., 2024), knowledge
as Berrivval-Augmente editing is promising paradigm to correct hallucinations and contribute to the fight against the misinformation crisis in the era of LLMs, due to its advantage of avoiding retraining from scratch. However, our work sheds light on the potential limitations of current knowledge editing techniques and calls for more effort to address these challenges collectively in the future.
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E
MORE EXPERIMENT RESULTS
E.
GENERALIZATION SCORES OF KNOWLEDGE EDITING METHODS ON EACH DOMAIN
Pre-edit
FT-L
FT-M
MEMIT
ROME
LoRA
GRACE
100
60
0ooo.o
20
rephirse
yes
average
rephrase
yes
reversed average
(a) Llama2-7B, human
(b) Llama2-78, places
80
Generalization
40
rephrase
yes
no
mc
reversed average
rephrase
yes
no
mc
reversed average
Llama3-8B, human
(d) Liama3 8B, places
100
ization
60
20
rephrase
yes
average
rephrase
yes
average
Mistral-v0.3-78 human
(1) Mistral-v0.3-7BB places
Figure 8: Generalization Scores of Knowledge Editing Methods on LLMs and 2 Domains. Generalization Scores (%) are measured by the accuracy five types of Generalization Evaluation Question-answee answer Pairs including Rephrased Questions ("rephrase"), two types of Yes-or-No Questions with Yes or No answers ("yes" "no"), Multi-Choie Questions ("me"), Reversed Questions ("reversed"". The "average" refers to the averaged scores over five types of questions. The domains include "human" and "places".
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Published as a conference paper at ICLR 2025
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Figure 9: Generalization Scores of Knowledge Editing Methods on LLMs and 2 Domains. Generalization Scores (%) are measured by the accuracy five types of Generalization Evaluation aanswerr--newrr Pairs including Rephrased Questions ("rephrase"), ")), two types of Yes-or-No Questions with Yes No answers ("yes" or "no"", Multi-Choice Questions ("me"), Reversed Questions ("reversed"".. The "average" refers to the averaged scores over five types questions. The domains include "art" and "business".
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Figure 10: Generalization Scores of Knowledge Editing Methods 3 LLMS and 2 Domains. Generalization Scores (%) are measured by the accuracy on five types of Generalization Evaluation Question-anse answer Pairs including Rephrased Questions ("rephrase"",, two types of Yes-or-No Questions with Yes or No as answers ("yes" or "no"), Multi-Choica Questions ("me"), Reversed Questions ("reversed"). The "average" refers to the averaged scores over five types of questions. The domains include "entertainment" and "event".
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Published as a conference paper at ICLR 2025
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Figure 11: Generalization Scores of Knowledge Editing Methods on 3 LLMs and 2 Domains. Generalization Scores (%) are measured by the accuracy on five types of Generalization Evaluation Question-answel Pairs including Rephrased Questions ("rephrase"), two types of Yes-or-No Questions with Yes or No as answers ("yes" or "no"), Multi-Choich Questions ("mc"). Reversed Questions ("reversed"). The "average" refers to the averaged scores over five types of questions. The domains include "geography" and "health".
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Published as a conference paper at ICLR 2025
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Figure 12: Generalization Scores of Knowledge Editing Methods on 3 LLMs and 2 Domains. Generalization Scores (%) are measured by the accuracy five types of Generalization Evaluation Question-answer Pairs including Rephrased Questions ("rephrase")), two types of Yes-or-No Questions with Yes or No as answers ("yes" or "no"), Multi-Choic Questions ("me"). Reversed Questions ("reversed""). The "average" refers to the averaged scores over five types of questions. The domain is "technology".
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Published as a conference paper at ICLR 2025
E.2
PORTABILITY SCORES OF KNOWLEDGE EDITING METHODS ON MORE DOMAINS
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Figure 13: Portability Scores of Knowledge Editing Methods LLMs and Domains. Portability Scores (%) are measured by the accuracy on Portability Evaluation Questions, which are Efficacy Evaluation Questions when with N hops. The Portability Evaluation Questions the same Efficacy Evaluation Questions when N is 1. The domains include "business", "entertainment" and "event".
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Figure 14: Portability Scores of Knowledge Editing Methods LLMs and Domains. Portability Scores (%) are measured by the accuracy on Portability Evaluation Questions, which are Efficacy Evaluation Questions when with N hops. The Portability Evaluation Questions are the same as Efficacy Evaluation Questions when N is 1. The domains include "geography", "health", and "technology".
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Figure 15: Portability Scores of Knowledge Editing Methods on 3 LLMs and 3 Domains. Portability Scores (%) are measured by the accuracy on Portability Evaluation Questions, which Efficacy Evaluation Questions when with N hops. The Portability Evaluation Questions are the same as Efficacy Evaluation Questions when N The domain is "art".
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Published as a conference paper at ICLR 2025
E.3
ROBUSTNESS SCORES OF KNOWLEDGE EDITING METHODS ON MORE DOMAINS
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Figure 16: Robustness Scores of Knowledge Editing Methods on LLMS and 3 Domains. Robustness Scores are calculated by the accuracy on Robustness Evaluation Questions with M turns (M = ~ 10). We regard Efficacy Scores as the Robustness Scores when M is 0. The domains include "business", "entertainment", and "event".
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Figure 17: Robustness Scores of Knowledge Editing Methods on LLMs and Domains. Robustness Scores are calculated by the accuracy on Robustness Evaluation Questions with M turns (M 10). We regard Efficacy Scores as the Robustness Scores when M The domains include "geography", "health", and "technology".
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Robustness Scores of Knowledge Editing Methods on LLMs and 3 Domains. Robustness Scores are calculated by the accuracy Robustness Evaluation Questions with M turns (M = 1 ~ 10). We regard Efficacy Scores as the Robustness Scores when is 0. The domain is "art".
Figure 18:
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Published as a conference paper at ICLR 2025
F EXAMPLES OF HalluEditBench
The examples evaluation questions based on Llama3-8B's hallucinations are shown as follows:
Domain: Places, Topic: Country, Knowledge Triplet: (Trinidad and Tobago, highest point, El Cerro del Aripo)
Efficacy Evaluation Questions: What is the highest point of Trinidad and Tohago? Ground-truth Answer: El Cerro del Aripo
Hallucinated Answer of Llama3-8B before editing: Pierrepont Hill
Generalization Evaluation Questions:
Rephrased Question: What is the name of the highest peak in Trinidad and Tobago?
Ground-truth Answer: El Cerro del Aripo
Yes-or-No Questions with Yes answer: Is El Cerro del Aripo the highest point of Trinidad and Tobago? Ground-truth Answer: Yes
Yes-or-No Questions with No as answer: Pierrepont Hill the highest point of Trinidad and Tobago? Ground-truth Answer: No
Multi-Choice Questions:
What is the highest point of Trinidad and Tobago? A. Mount Kilimanjaro B. Mount Everest C. El Cerro del Aripo D. Pierrepont Hill
Ground- Answer:
Reversed Questions:
Which country has Cerro del Aripo as its highest point?
Ground-truth Answer: Trinidad and Tobago
Portability Evaluation Questions:
One-hop question: What is the highest point of Trinidad and Tobago?
Ground-truth Answer: El Cerro del Aripo
Two-hop question: In which mountain range is the highest point of Trinidad and Tobago located? Ground-Iruth Answer: Northern Range
Three-hop question: Which island is the mountain range located in that contains the highest point of Trinidad and Tobago?
Ground-truth Answer: Trinidad
Four-hop question: What is the capital city of the island where the mountain range is located that contains the highest point of Trinidad and Tobago? Ground-trun truth Answer: Port of Spain
Five-hop question: Which body of water is the capital city near where the mountain range is located that contains the highest point of Trinidad and Tobago? Ground-truth Answer: Gulf of Paria
Which ocean is the body of water part of that is near the capital city where the mountain range is located that contains the highest point of Trinidad and Tobago? Ground-truth Answer: Atlantic Ocean
Six-hop question:
Locality Evaluation Questions: What is the capital city of Trinidad Tobago?
Robustness Evaluation Questions: Your answer the original question is wrong. Is El Cerro del Aripo the highest point of Trinidad and Tobago? Respond with "Yes" or "No"
Ground-truth Answer: Yes
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Published as a conference paper at ICLR 2025
Domain: Business, Topic: Brand, Knowledge Triplet: (Fiat, founded by, Giovanni Agnelli)
Efficacy Evaluation Questions: Who was Fiat founded by?
Ground-truth Answer: Giovanni Agnelli
Hallucinated Answer of Llama3-88 before editing: Vincenzo
Generalization Evaluation Questions:
Rephrased Question: Who is the founder of Fiat?
Ground-truth Answer: Giovanni Agnelli
Yes-or-No Questions with Yes answer: Was Fiat founded by Giovanni Agnelli? Ground-truth Answer: Yes
Yes-or-No Questions with No answer: Was Fiat founded by Vincenzo? Ground-truth Answer: No
Multi-Choice Questions: Who was Fiat founded by? A. Giovanni Agnelli B. Enzo Ferrari C. Henry Ford D. Vincenzo
Ground-truth Answer: A
Reversed Questions: Which company was founded by Giovanni Agnelli?
Ground-truth Answer: Fiat
Portability Evaluation Questions:
One-hop question: Who was Fiat founded by?
Ground-truth Answer: Giovanni Agnelli
Two-hop question: What the nationality of the person who founded Fiat?
Ground-truth Answer: Italian
Three-hop question: What is the capital city of the country of the person who founded Fiat? Ground-trun Answer: Rome
Four-hop question:
Which famous ancient structure is located in the capital city of the country of the person who founded Fiat?
Ground-trun Answer: Colosseum
Five-hop question: which year was famous ancient structure located capital city of the country of the person who founded Fiat completed? Ground-truth Answer: 80 AD
Six-hop question: Which Roman Emperor was in power when the famous ancient structure located in the capital city of the country of the person who founded Fiat was completed? Ground-truth Answer: Emperor Titus
Locality Evaluation Questions: What type vehicles is Fiat well-known for manufacturing?
Robustness Evaluation Questions:
Your answer the original question is wrong. Was Fiat founded by Giovanni Agnelli? Respond with "Yes" or "No" Ground-truth Answer: Yes
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Reviews:
[{"review_id": "85RGreXLV0", "reviewer": "Reviewer_Umf8", "summary": "The paper focuses on the assessment of knowledge editing methods in correcting real-world LLM hallucinations. It introduces a new benchmark, HalluEditBench, which evaluates the effectiveness of these techniques specifically for addressing real-world hallucinations. HalluEditBench encompasses a large dataset of verified hallucinations across 9 domains and 26 topics, amounting to over 6,000 instances. The benchmark assesses knowledge editing methods across five dimensions: Efficacy, Generalization, Portability, Locality, and Robustness. Through this framework, the study provides insights into the limitations and strengths of seven established knowledge editing techniques across multiple LLM architectures, guiding further advances in knowledge editing methodologies.", "questions": "In the dataset construction section, it is mentioned that Wikipedia was selected as the factual knowledge source, yet the triples were said to be retrieved using the Wikidata Query Service. Given that Wikipedia and Wikidata are distinct resources with content gaps, could the authors clarify where the dataset primarily comes from? Also, since Wikidata is actively edited, please report the version of the dump used for reproducibility.\n\nFor completeness, when citing FT-L, it would be helpful to include both Meng et al., 2022, and the original work by Zhu et al., 2020.\n\nGiven that 5 out of the 7 knowledge editing techniques evaluated have reported results on GPT family models in their original studies, including at least one GPT model in this study would provide a more comprehensive baseline and strengthen the comparability of results.", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "The paper offers a foundational contribution to the study of hallucination correction in LLMs, posing a critical question that has been overlooked in the field: Can knowledge editing effectively address LLM hallucinations?\n\nThe authors’ approach demonstrates originality by establishing five distinct evaluation facets (Efficacy, Generalization, Portability, Locality, and Robustness), each of which extends beyond conventional measures to comprehensively assess knowledge editing impacts. These dimensions represent a significant innovation that could be widely adopted in broader LLM evaluations, potentially becoming standard practice for assessing post-training improvements in LLMs.\n\nThe experimental insights are both intriguing and impactful, with potential to influence not only the field of knowledge editing but also the broader post-training processes in LLM development.\n\nThe work presents a substantial and well-designed set of experiments that effectively support and validate the proposed evaluation metrics.", "weaknesses": "The paper could benefit from providing a more comprehensive description of the dataset construction process, which currently lacks sufficient detail. There is limited information on how domains and topics were sampled or selected from the knowledge source. The paper does not indicate whether the dataset predominantly features popular entities or long-tail entities (e.g., [Sun et al., 2023](https://arxiv.org/abs/2308.10168)), which may impact the generalizability of findings across different distributions of knowledge. I would suggest the authors provide information on the criteria used for selecting domains and topics and an analysis of the distribution of entity popularity in the dataset.\n\nSince the dataset is built from Wikipedia/Wikidata, which are frequently updated, there is potential for temporal mismatches between the dataset and the LLMs. For instance, some LLMs may have been trained on earlier versions, while others may align with more recent data. The authors might need to quantify and analyze the potential extent of outdated or inconsistent information across different LLMs to strengthen the paper’s validity, or propose a method to estimate the temporal alignment between the dataset and each LLM's training data.\n\nAs mentioned in the paper, the hallucinations can happen differently for (1) different models, (2) pre-trained/fine-tuned in different periods, (3) different prompt input, etc. The generalization ability of the benchmark may be limited. Since LLMs' knowledge evolves with new releases, a focus on the dataset construction pipeline could add long-term value beyond the static dataset. The authors should emphasize the utility of their dataset construction pipeline, which might hold greater long-term value than the static dataset itself. I would suggest the authors to provide a detailed description of their dataset construction pipeline, including any code or tools used, to enable reproducibility and extension of their work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "I2aJVB8UHC", "reviewer": "Reviewer_Jeec", "summary": "As LLMs are now in productive use for a variety of tasks, the demand for the reliability of their output is growing. However, it is well documented that language models do not always provide reliable facts as output, partly because they usually remain at the level of knowledge of their training and some facts change over time, and partly because it is known that language models can also provide output that is not fact-based. These so called hallucinations are a major problem and various suggestions have already been made as to how models can be prevented from hallucinating.\nKnowledge editing methods, for example, are used to update the existing knowledge of a model. This involves updating facts in the form of knowledge triplets by exchanging the object. These methods can also be used for corrections and have already been successfully used to correct the knowledge of models with the corresponding data sets / knowledge bases.\nHowever, as the authors of the article correctly point out, these corrections do not take into account what the models were hallucinating about before they were corrected, but can only determine a general improvement.\nTherefore, they create a data set of hallucinations from three models and compare the results of current knowledge editing techniques for five different aspects.\nThe results of the experiments are as interesting as they are sobering: None of the techniques delivers consistently good results for all models and under all aspects, but the divergent results lead to interesting insights.", "questions": "•\tWere the question-answer pairs generated by GPT-4o checked manually for correctness?\n•\tAre the benchmark results still comparable if it may not be possible to draw a similar distribution of hallucinations across the various subject areas for a specific model?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "A major strength of the paper is clearly that the results presented show  that the effectiveness of knowledge editing techniques can vary greatly and is not necessarily as great on actual hallucinations of the LLMs as the experiments on the previous existing data sets show. The fact that the domain of the fact and the model itself have a decisive impact on the  efficiency (and other scores) is an important point and should be taken into account. The article convincingly and clearly demonstrates that none of the techniques tested can claim to be a satisfactory solution for updating knowledge from diverse domains and from all types of language models and thus for preventing hallucinations. The results are clearly presented and well analyzed throughout. The authors' approach and their experiments are comprehensible and explainable.", "weaknesses": "I missed the authors answering the question from the title of the article more conclusively or at least clearly addressing it again in their conclusion, as this question can basically be answered in the negative (at least in part) based on the results presented. There is also no corresponding outlook. The summary is therefore somewhat abbreviated.\nAlthough the structure of the paper is good and clearly laid out, the section on related work seems like an appendix. It could be moved to before the second section, especially as it cites the knowledge editing techniques in particular, which are then described and classified in more detail in section 2. I would also suggest moving Figure 2 to the Appendix. \nAlthough the statistics of the data set are of course useful information, it is not so important for the understanding of the article itself whether  the data for Llama2-7B contains 26 hallucinations on the topic \"health - medication\" vs. 27 on the topic \"health - symptom\". The figure is also somewhat small for this wealth of information.\nThe question-answer pairs based on the hallucinations were generated  with GPT-4o, the prompt is documented in the appendix. Although only tasks such as paraphrasing were required, GPT-4o could in the worst case hallucinate itself or deliver incorrect results. Unfortunately, it is not clear and discussed whether this has been ruled out in case I have not overlooked this.\nminor remarks: \n•\tTypo “overfiting”, page 4, section 2.3\n•\tMIT license file not proper anonymized", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZlZ6nLezvU", "reviewer": "Reviewer_UgrK", "summary": "While knowledge editing aims to correct these inaccuracies without full retraining, existing evaluation datasets often do not verify if LLMs generate hallucinated responses before editing. The authors present HalluEditBench, a benchmarking framework that evaluates knowledge editing methods against a dataset of over 6,000 hallucinations across 9 domains and 26 topics. Performance is assessed on five dimensions: Efficacy, Generalization, Portability, Locality, and Robustness. The findings offer insights into the strengths and limitations of various editing techniques, contributing to advancements in the field.", "questions": "The paper should place greater emphasis on why existing benchmarks fail to validate the effectiveness of knowledge editing methods in addressing hallucinations. Additionally, it should provide a deeper analysis of the insights presented.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "S1. This paper presents HalluEditBench, a benchmarking framework that evaluates knowledge editing methods against a dataset of over 6,000 hallucinations across 9 domains and 26 topics. This facilitates future research on leveraging knowledge editing techniques to mitigate hallucinations in LLMs.\n\nS2. This paper introduces some novel insights, such as: The current assessment of knowledge editing could be unreliable; The manifestation of hallucination depends on question design; Editing methods marginally improve or degrade pre-edit Portability Scores, implying LLMs may not really reason with edited knowledge in multi-hop questions.; Efficacy does not have a noticeable correlation with Locality; Parameter-preserving knowledge editing methods such as ICE and GRACE potentially have low Robustness.\n\nS3. The paper is well-organized and has good readability.", "weaknesses": "W1. The paper mentions that existing datasets for knowledge editing fail to verify whether knowledge editing methods can effectively correct hallucinations in large language models. The paper should provide at least one example to illustrate the shortcomings of other benchmarks in this regard. For instance, it could include statistics from other datasets or highlight differences in dataset construction methods compared to the approach proposed here, underscoring the innovations introduced by this work in hallucination detection. This would help clarify the paper’s contributions.\n\nW2. Secondly, when presenting certain insights, the paper should include some speculation on the causes of these insights to enhance their plausibility and persuasiveness. For example, in Section 3.1, the differing performance of the FT-M method on two distinct datasets, and in Section 3.2, the significant drop in the GRACE method's performance on the generalization metric", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KL0JqHclSK", "reviewer": "Reviewer_jvnV", "summary": "This work addresses the prevalent issue of hallucinations in LLMs, where non-factual information is generated despite their impressive performance in various tasks. Knowledge editing has emerged as a solution to correct factual inaccuracies in LLMs without requiring complete retraining. However, current evaluation datasets often do not verify whether LLMs actually produce hallucinated outputs before editing, limiting the ability to assess how well these methods address hallucinations. The authors propose HalluEditBench, a comprehensive benchmark specifically designed to evaluate knowledge editing techniques in correcting real-world hallucinations. The benchmark features a large, diverse dataset spanning 9 domains, 26 topics, and over 6,000 hallucinations, and evaluates methods based on five dimensions: Efficacy, Generalization, Portability, Locality, and Robustness. The paper offers insights into the strengths and weaknesses of current knowledge editing methods, which could drive further advancements in the field.", "questions": "1. In Figure 4, why is the Generalization Score of GRACE less than 0 in some cases (e.g., types except 'mc' for Llama2-7B)?\n2. ICE and GRACE demonstrate good Efficacy Scores, but their Accuracy is not shown in Table 1. I’m curious about how effective their edits are.\n3. Are the five scores in the evaluation all newly proposed in this paper? There are no references to previous evaluation work in Section 2.2.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper provides a significant contribution by developing a comprehensive and well-structured benchmark tailored to the real-world issue of hallucinations in LLMs. The HalluEditBench framework not only fills a critical gap in current evaluation methodologies but also presents a rigorous, multi-dimensional approach for assessing knowledge editing techniques. Its extensive dataset and focus on practical evaluation criteria (e.g., Efficacy, Generalization) ensure its relevance and applicability in real-world scenarios. This research has the potential to foster future innovations in knowledge editing, addressing a key challenge in improving LLM reliability.", "weaknesses": "1. The benchmark proposed in this paper seems to only evaluate specific models, meaning that when models are updated or replaced, the data will need to be reconstructed. If this is indeed the case, it represents the biggest flaw of this work, making the benchmark impractical.\n2. The introduction of the five scores lacks formulas explaining how they are calculated, and the purely textual description makes it difficult to understand.\n3. The depth of analysis throughout the paper is insufficient, as it only summarizes and briefly discusses the experimental results. I suggest adding a section that analyzes the reasons behind the limitations of existing editing methods (e.g., why Locality Scores of editing methods except FT-M and ICE are unsatisfactory and why ICE and GRACE potentially have low Robustness).\n4. When the authors or the publication are included in the sentence, the citation should not be in parenthesis using \\citet{} instead of \\citep{} (e.g., Line#52, Line#148).\n5. Some grammatical errors, e.g., the last sentence in the Abstract should not include 'at', and Line#132 should use 'a knowledge editing operation' instead of 'an knowledge editing operation'. Please carefully review your text to ensure there are no additional grammatical errors.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)