Pith. sign in

REVIEW 5 major objections 5 minor 2 cited by

COMPKE: Complex Question Answering under Knowledge Editing

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper introduces COMPKE, a benchmark of 11,924 complex questions that combine one-to-many facts with logical operations, and shows that current knowledge-editing methods lose most of their accuracy on these questions.

desk verdict COMPKE is a meaningful new KE benchmark with promising failure-mode analysis, but the evaluation has a knowledge-coverage confound and an inflated headline metric that need fixing. read the letter →

arxiv 2506.00829 v2 pith:52HSDYPN submitted 2025-06-01 cs.CL cs.AIcs.LG

Keyuan Cheng , Zijian Kan , Zhixian He , Zhuoran Zhang , Muhammad Asif Ali , Ke Xu , Lijie Hu , Di Wang This is my paper
classification cs.CLcs.AIcs.LG
keywords knowledgeeditingcomplexquestionansweringbenchmarkone-to-manyrelationslogicalreasoningmulti-hopQALLMevaluationWikidata
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that knowledge-editing benchmarks built on linear multi-hop questions do not measure what happens when a model must actually use an edited fact in a realistic query. It introduces COMPKE, a Wikidata-derived benchmark of 11,924 questions that require one-to-many knowledge, set intersections and unions, and conditional confirmation, with edits that add, delete, or retain entities. Evaluating ROME, MEMIT, MeLLo, and PokeMQA on five language models, the paper finds accuracy far below previous benchmarks and identifies distinct failure modes: parameter-based methods overfit the injected fact on small models, while memory-based methods skip logical steps during question decomposition. The paper's conclusion is that current KE methods are not yet reliable for complex question answering.

What carries the argument

The paper formalizes a complex question as a graph-structured object Q=(S,L), where S is a collection of intermediate entity sets and L is a set of reasoning links: knowledge mapping (moving from one entity set to adjacent entities through a relation), condition confirmation (filtering an entity set by a relation to a target entity), and logical links (set intersection and union). This representation is paired with a one-to-many edit formalism e=(s,r,O→O'), where the object set of a fact is updated by addition, deletion, or retention of entities. That combination is what carries the argument: it lets the benchmark construct questions that cannot be solved by sequential one-to-one chaining and require combining edited and retained facts.

What would settle it

Re-run the QWEN2.5-3B evaluation on a COMPKE variant built from Wikidata facts without the GPT-3.5 recall filter; if accuracy rises substantially, the filter—not the editing method—was the cause of the 3.83 result. Separately, have GPT-4o-mini answer questions phrased by a different generator; a large score drop would indicate that generating and evaluating with the same model inflated its results.

Watch

Extended reading notes

Core claim

The central claim is that complex questions—those requiring one-to-many relations, logical operations like intersection and union, and conditional confirmation—are the right test for knowledge editing, and that on such a test the main KE methods largely fail. On COMPKE, the best memory-based method reaches 39.47 on GPT-4O-MINI but falls to 3.83 on QWEN2.5-3B, and parameter-based methods show apparent gains on small models that trace to repeating the edited content rather than reasoning with it. The paper further shows that decomposition-based methods omit the intersection step when their prompt examples lack logical operations, and that all methods degrade sharply as the batch of simultaneous edits grows.

Load-bearing premise

The benchmark's fairness rests on the assumption that facts filtered by GPT-3.5-turbo-instruct's recall are equally knowable to all evaluated models; if smaller models never learned those facts in pretraining, their low scores conflate missing background knowledge with failed editing.

Editorial extensions

If this is right

  • Knowledge-editing benchmarks should include one-to-many relations and logical operations, because linear multi-hop questions overestimate method performance.
  • Decomposition-based editing methods need prompt examples that cover logical operations; otherwise they silently drop the intersection or union step.
  • Augment/retain accuracy scores on small models can be inflated by overfitting, so editing methods should be checked for whether they reason or merely repeat edited content.
  • Batch editing beyond about 100 simultaneous edits destabilizes parameter-based methods, often producing incoherent output.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the recall filter in construction uses one model, the apparent gap between strong and weak models may partly reflect pretraining coverage; a per-model filtering variant would separate knowledge coverage from editing failure.
  • GPT-4o-mini both generates the natural-language questions and is evaluated on them, so its scores could be inflated by phrasing familiarity; regenerating questions with another model would test this.
  • The paper's own limitation note that edits are random counterfactuals suggests a curated version with realistic knowledge updates could rank methods differently.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper introduces COMPKE, a benchmark of 11,924 complex question-answering instances under knowledge editing, built from Wikidata. Complex questions are formalized as graph-structured reasoning problems over entity sets, with reasoning links that include knowledge mapping, condition confirmation, intersection, and union. The construction pipeline samples one-to-one and one-to-many facts, applies counterfactual edits of addition/deletion/retention, filters conflicting edits, and uses GPT-4o-mini to render structured questions in natural language. The authors evaluate ROME, MEMIT, MeLLo, and PokeMQA on five LLMs (Qwen2.5-3B/7B, Llama-3.1-8B, GPT-3.5-Turbo, GPT-4o-mini) under edit batches of size 1, 100, 1000, and 3000, reporting Augment, Retain, and overall Accuracy. They report that methods perform poorly on COMPKE, analyze overfitting in parameter-based methods and omission of logical steps in decomposition-based methods, and claim COMPKE is more challenging than MQuAKE.

Significance. If the evaluation is valid, COMPKE fills a genuine gap: existing knowledge-editing benchmarks rely on linear multi-hop questions with one-to-one relations, while COMPKE targets one-to-many relations, logical operations, and richer edit types. The formalization of complex questions as entity sets with generalized reasoning links is a useful contribution, and the released dataset and detailed construction pipeline are valuable assets for the community. The paper also provides a thoughtful qualitative analysis of failure modes, including the overfitting behavior of parameter-based methods and MeLLo's omission of intersection steps. However, the central empirical claims are currently undermined by several evaluation issues: the fact filter is applied to only one model, the headline accuracy metric is acknowledged to be inflated by overfitting, the comparison with MQuAKE mixes different metrics, and the abstract misattributes a key result. These issues are fixable but require additional experiments and reporting.

major comments (5)
  1. [§4.1, Step 2 and Appendix A.2] The fact-filtering stage uses only GPT-3.5-turbo-instruct to decide which Wikidata triples are retained, yet the evaluated models include Qwen2.5-3B/7B, Llama-3.1-8B, and GPT-4o-mini. The assertion in Appendix A.2 that 'all evaluated edits pertain to knowledge the model actually possesses' is therefore only validated for GPT-3.5-turbo-instruct, not for the other target models. If a triple is obscure to a smaller open model, that model cannot answer the complex question even after a perfect edit, so its low accuracy is conflated with editing failure. Because the paper's headline cross-model contrast (e.g., 3.83 vs. 39.47) depends on this assumption, the paper must report pre-edit (unedited) accuracy for each evaluated model on COMPKE, or apply a per-model recall filter, to separate knowledge coverage from editing performance.
  2. [Table 3 and Figure 8] The Augment metric is demonstrably inflated by overfitting, as the authors themselves show in Figure 8: after MEMIT editing, the model outputs the newly injected set {Wharton School, University of Cambridge} without performing the required intersection, and Aug counts the correct entity as correct while ignoring the extraneous entity. Since Accuracy is defined as the average of Aug and Ret (Section 5.1), the headline Acc values in Table 3 inherit this inflation, particularly for parameter-based methods on small models. The paper should report an exact-match or precision-style metric that penalizes extraneous entities, and should interpret the current Aug/Acc numbers with this acknowledged caveat rather than presenting them as the primary result.
  3. [Figure 5 and Appendix D.3] The claim that COMPKE is more challenging than MQuAKE is based on a comparison of incompatible metrics. MQuAKE is evaluated with M-Acc, which requires exact match of the final answer (Eq. 3 in Appendix D.3), whereas COMPKE results use the set-based Aug/Ret/Accuracy defined in Eqs. 1-2. Lower scores on COMPKE could simply reflect a stricter metric. The authors should re-evaluate both datasets with an identical metric (e.g., exact-match accuracy on the final answer set) before concluding that COMPKE is more challenging.
  4. [Abstract and Table 3] The abstract states 'MeLLo attains an accuracy of 39.47 on GPT-4O-MINI', but Table 3 reports MeLLo's 1-edited Accuracy on GPT-4O-MINI as 23.63, with 39.47 belonging to PoKeMQA. This misattributes the paper's headline quantitative result and must be corrected, since the sentence is used to motivate the central claim about cross-model performance disparity.
  5. [Table 3] All results are reported as single runs without variance estimates, even though both parameter-based editing and API-based LLM inference involve stochasticity. Differences of a few points, such as MeLLo vs. PoKeMQA on several rows, may not be meaningful without multiple seeds or confidence intervals. At minimum, the authors should run each configuration multiple times and report standard deviations, or justify why single runs are sufficient.
minor comments (5)
  1. [§5.2] The subsection heading 'Comparision with other Datasets' contains a typo; it should read 'Comparison with Other Datasets'.
  2. [Figure 4 and Figure 9] Some figure labels appear as garbled unicode sequences (e.g., in the caption of Figure 4). The authors should regenerate the figures to ensure all text is readable and correctly rendered.
  3. [Throughout] Model names are used inconsistently: 'QWEN2.5-3B' and 'Qwen2.5-3B' both appear, and 'PoKeMQA' is sometimes written as 'PokeMQA'. Please standardize.
  4. [Appendix D.3, Eq. (3)] Equation (3) is typeset with broken control characters (e.g., the '1' and bracket glyphs). The M-Acc formula should be reset cleanly.
  5. [§4.1, Step 6] GPT-4o-mini is used both to generate natural-language questions and as one of the evaluated models. While not fatal, this self-reference could inflate GPT-4o-mini's scores through familiar phrasing; a brief discussion or a small human-validated subset would strengthen the benchmark's neutrality.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: COMPKE results are measured benchmark outcomes, not fitted predictions; the generator/evaluator overlap and single-model fact filtering are validity concerns, not circular reductions.

full rationale

This paper constructs a benchmark and reports evaluation results; it does not fit a parameter and then predict a closely related quantity. The derivation chain is: define complex questions as graph-structured reasoning problems (Section 3.1), build COMPKE from Wikidata via template instantiation and LLM-based paraphrasing (Section 4.1), then run standard KE baselines and report Aug/Ret/Acc (Section 5). None of the reported accuracies is a renamed fit of the construction inputs, and no equation reduces to another by construction. The main empirical claims—that current KE methods struggle on complex questions, that memory-based methods depend on instruction-following, and that parameter-based methods overfit—are measurements, not consequences of a definition. The paper does contain two mild self-reference issues that affect interpretation but not circularity. First, Appendix A.2 asserts that filtering ensures 'all evaluated edits pertain to knowledge the model actually possesses,' but Section 4.1 Step 2 filters facts using only GPT-3.5-TURBO-INSTRUCT, so this is not established for QWEN2.5-3B, QWEN2.5-7B, or LLAMA-3.1-8B; low scores on those models could partly reflect pretraining coverage rather than editing failure. Second, GPT-4o-mini generated the natural-language question variants (Step 6) and is also one of the five evaluated models, which may inflate its relative scores. These are external-validity confounds, not circular reductions. The many self-citations in the related work (e.g., MQAKEAL, Cheng et al., Zhang et al.) are not load-bearing for the benchmark construction or the main results. The abstract's attribution of 39.47 to MeLLo is a factual error—the table attributes it to PoKeMQA—but that is a reporting mistake, not evidence of circularity. Overall, the central claim is self-contained and independently measurable, so the circularity score is low.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on data construction choices rather than fitted parameters. The only model-dependent assumptions are the use of GPT-3.5-turbo-instruct for fact filtering and GPT-4o-mini for question phrasing, which are ad hoc choices that bias the benchmark construction. No new theoretical entities are introduced.

assumptions (4)
  • domain assumption Wikidata triples are reliable ground truth for the knowledge base.
    Section 4.1 Step 1: the benchmark assumes factual correctness of extracted triples, which is not verified against a human-annotated gold set.
  • ad hoc to paper GPT-3.5-turbo-instruct's ability to recall a fact is a valid proxy for a target LLM's knowledge.
    Section 4.1 Step 2: this filter selects the knowledge base, so all downstream evaluation depends on it.
  • ad hoc to paper The natural-language questions generated by GPT-4o-mini preserve the exact logical structure of the structured templates.
    Section 4.1 Step 6: no human validation is reported, so errors in phrasing could change the reasoning required.
  • domain assumption Standard set operations (intersection, union) on Wikidata object sets are well-defined and the model's string outputs can be mapped to these sets.
    Section 3.1 and Appendix D.3: evaluation relies on string matching between model output and answer sets.

how reviews work

0 comments
Cite this review

Pith. "Pith review of COMPKE: Complex Question Answering under Knowledge Editing." pith.science (2026). https://pith.science/paper/52HSDYPN

@misc{pith2026250600829,
  author       = {Pith},
  title        = {Pith review of: COMPKE: Complex Question Answering under Knowledge Editing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/52HSDYPN}},
  note         = {Machine review of arXiv:2506.00829}
}
read the original abstract

Knowledge Editing, which efficiently modifies the knowledge in large language models, has gathered great attention. Current benchmarks primarily use multi-hop question answering to assess and analyze newly injected or updated knowledge. However, we argue that these benchmarks fail to effectively evaluate how well the updated models apply this knowledge in real-life scenarios, particularly when questions require complex reasoning, involving one-to-many relationships or multi-step logical intersections. To fill in this gap, we introduce a new benchmark, COMPKE: Complex Question Answering under Knowledge Editing, which includes 11,924 complex questions that reflect real-life situations. We conduct an extensive evaluation of four knowledge editing methods on COMPKE, revealing that their effectiveness varies notably across different models. For instance, MeLLo attains an accuracy of 39.47 on GPT-4O-MINI, but this drops sharply to 3.83 on QWEN2.5-3B. We further investigate the underlying causes of these disparities from both methodological and model-specific perspectives. The datasets are available at https://github.com/kzjkzj666/CompKE.

Figures

Figures reproduced from arXiv: 2506.00829 by the authors.

Figure 1
Figure 1. (a) An example of a multi-hop question in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. An example of a complex question under knowledge editing. Knowledge editing occurs in the first sub-question, where the filming location of Chris￾time is modified from { Los Angeles} to {San Fran￾cisco, Los Angeles, New York}. ment indicating that the subject entity s is con￾nected to the object entity o via relation r. To ac￾commodate one-to-many relationships, we gener￾alize the knowledge instance to the form (s, … view at source ↗
Figure 3
Figure 3. The construction process of COMPKE. eryday contexts. The complete set of relation tem￾plates used in COMPKE is listed in Appendix Ta￾ble 9. Step 2: Sampling Facts. After selecting the re￾lation templates, we construct the knowledge base D, prioritizing widely recognized facts over ob￾scure ones. We sample single-hop knowledge triples from Wikidata based on the chosen relation templates and rank them according to the… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Variation of Accuracy (Acc) across QWEN2.5-3B, QWEN2.5-7B, and LLAMA-3.1-8B models with varying edit numbers. Results for GPT-3.5-TURBO and GPT-4O-MINI are provided in Appendix E.2. through APIs, parameter-based knowledge editing methods cannot be applied to them. Eval…
Figure 5
Figure 5. Figure 5: Performance comparison of MeLLo and PoKeMQA on the MQuAKE-T, MQuAKE-CF-3k, and COMPKE datasets on GPT-4O-MINI, with COMPKE presenting more challenging than previous datasets. tional confirmation operations, such as logical in￾tersections. As a result, MeLLo fails to ge…
Figure 6
Figure 6. Figure 6: Relations and their frequencies in C [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Variation of Accuracy (Acc) across GPT-3.5-Turbo and GPT-4o-mini models with varying edit numbers. educated at educated at ∧ Ted Schroeder Laurene Powell Jobs {Stanford University, Glendale High School} {University of Pennsylvania, Stanford University, The Wharton Scho…
Figure 8
Figure 8. Figure 8: An example of MEMIT applied to Qwen2.5-3B. The correct solution should be for the model to [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: When the edit batch size increases, the MEMIT method outputs a large amount of gibberish after models [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Some typical reasoning structure in COMPKE [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Compositional Architecture of Regret in Large Language Models

    cs.CL 2025-06 reject novelty 5.0 of 10

    The paper claims that regret in LLMs is encoded by interacting neuron groups detectable in the final hidden layer, using new S-CDI, RDS, and GIC metrics.

  2. Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images

    cs.AI 2025-06 reject novelty 4.0 of 10

    SHE lowers behavioral hallucination scores by about 10 percent by detecting low visual-textual similarity and projecting out the hallucinated direction in embedding space.

Reference graph

Works this paper leans on

68 extracted references · 12 canonical work pages · cited by 2 Pith papers

  1. [1]

    URL: " 'urlintro :=

    ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Abhinav Jauhri Abhimanyu Dubey et al. 2024. https://api.semanticscholar.org/CorpusID:271571434 The llama 3 herd of models . ArXiv, abs/2407.21783

    arXiv 2024

  4. [4]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

    arXiv 2023

  5. [5]

    Muhammad Asif Ali, Nawal Daftardar, Mutayyaba Waheed, Jianbin Qin, and Di Wang. 2024. http://arxiv.org/abs/2409.12257 Mqa-keal: Multi-hop question answering under knowledge editing for arabic language

    arXiv 2024

  6. [6]

    Muhammad Asif Ali, Yifang Sun, Bing Li, and Wei Wang. 2020. Fine-grained named entity typing over distantly supervised data based on refined representations. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34(05), pages 7391--7398

    work page 2020

  7. [7]

    Muhammad Asif Ali, Yifang Sun, Bing Li, and Wei Wang. 2021. Fine-grained named entity typing over distantly supervised data via refinement in hyperbolic space. CoRR

    work page 2021

  8. [8]

    Junwei Bao, Nan Duan, Zhao Yan, Ming Zhou, and Tiejun Zhao. 2016. Constraint-based question answering with knowledge graph. In Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, pages 2503--2514

    work page 2016

Show all 68 references
  1. [9]

    Yuchen Cai, Ding Cao, Rongxi Guo, Yaqin Wen, Guiquan Liu, and Enhong Chen. 2024. http://arxiv.org/abs/2403.14381 Editing knowledge representation of language model via rephrased prefix prompts

    2024 arXiv

  2. [10]

    Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin, Yuxuan Zhai, Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, and Di Wang. 2024 a . Leveraging logical rules in knowledge editing: A cherry on the top. arXiv preprint arXiv:2405.15452

    2024 arXiv

  3. [11]

    Keyuan Cheng, Gang Lin, Haoyang Fei, Lu Yu, Muhammad Asif Ali, Lijie Hu, Di Wang, et al. 2024 b . Multi-hop question answering under temporal knowledge editing. arXiv preprint arXiv:2404.00492

    2024 arXiv

  4. [12]

    Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2023. http://arxiv.org/abs/2307.12976 Evaluating the ripple effects of knowledge editing in language models

    2023 arXiv

  5. [13]

    Fernanda De La Torre, Cathy Mengying Fang, Han Huang, Andrzej Banburski-Fahey, Judith Amores Fernandez, and Jaron Lanier. 2024. Llmr: Real-time prompting of interactive worlds using large language models. In Proceedings of the CHI Conference on Human Factors in Computing Syste...

    2024

  6. [14]

    Jingcheng Deng, Zihao Wei, Liang Pang, Hanxing Ding, Huawei Shen, and Xueqi Cheng. 2024. http://arxiv.org/abs/2405.15349 Everything is editable: Extend knowledge editing to unstructured data in large language models

    2024 arXiv

  7. [15]

    Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. http://arxiv.org/abs/2305.14314 Qlora: Efficient finetuning of quantized llms

    2023 arXiv

  8. [16]

    Xiang Fang, Daizong Liu, Wanlong Fang, Pan Zhou, Yu Cheng, Keke Tang, and Kai Zou. 2023. Annotations are not all you need: A cross-modal knowledge transfer network for unsupervised temporal sentence grounding. In Findings of the Association for Computational Linguistics: EMNLP...

    2023

  9. [17]

    Javier Ferrando, Gabriele Sarti, Arianna Bisazza, and Marta R Costa-juss \`a . 2024. A primer on the inner workings of transformer-based language models. arXiv preprint arXiv:2405.00208

    2024 arXiv

  10. [18]

    Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, and Xin Wang. 2023. Pokemqa: Programmable knowledge editing for multi-hop question answering. arXiv preprint arXiv:2312.15194

    2023 arXiv

  11. [19]

    Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang, and Nanyun Peng. 2024. http://arxiv.org/abs/2401.04700 Model editing harms general abilities of large language models: Regularization to the rescue

    2024 arXiv

  12. [20]

    Willis Guo, Armin Toroghi, and Scott Sanner. 2024. Cr-lt-kgqa: A knowledge graph question answering dataset requiring commonsense reasoning and long-tail knowledge. arXiv preprint arXiv:2403.01395

    2024 arXiv

  13. [21]

    Akshat Gupta, Anurag Rao, and Gopala Anumanchipalli. 2024. Model editing at scale leads to gradual and catastrophic forgetting. arXiv preprint arXiv:2401.07453

    2024 arXiv

  14. [22]

    Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2022. https://api.semanticscholar.org/CorpusID:253735429 Aging with grace: Lifelong model editing with discrete key-value adaptors . ArXiv, abs/2211.11031

    2022 arXiv

  15. [23]

    Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun. 2024 a . Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models. Advances in Neural Information Processing Systems, 36

    2024

  16. [24]

    Peter Hase, Thomas Hofweber, Xiang Zhou, Elias Stengel-Eskin, and Mohit Bansal. 2024 b . Fundamental problems with model editing: How should rational belief revision work in llms? arXiv preprint arXiv:2406.19354

    2024 arXiv

  17. [25]

    Bing He, Mustaque Ahamad, and Srijan Kumar. 2023. https://doi.org/10.1145/3543507.3583388 Reinforcement learning-based counter-misinformation response generation: A case study of covid-19 vaccine misinformation . In Proceedings of the ACM Web Conference 2023, WWW '23, page 269...

    2023

  18. [26]

    Yihuai Hong, Yuelin Zou, Lijie Hu, Ziqian Zeng, Di Wang, and Haiqin Yang. 2024. Dissecting fine-tuning unlearning in large language models. arXiv preprint arXiv:2410.06606

    2024 arXiv

  19. [27]

    Cheng-Hsun Hsueh, Paul Kuo-Ming Huang, Tzu-Han Lin, Che Wei Liao, Hung-Chieh Fang, Chao-Wei Huang, and Yun-Nung Chen. 2024. https://doi.org/10.18653/v1/2024.findings-emnlp.550 Editing the mind of giants: An in-depth exploration of pitfalls of knowledge editing in large languag...

    2024 doi

  20. [28]

    Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

    Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. http://arxiv.org/abs/2106.09685 Lora: Low-rank adaptation of large language models

    2021 arXiv

  21. [29]

    Lijie Hu, Liang Liu, Shu Yang, Xin Chen, Hongru Xiao, Mengdi Li, Pan Zhou, Muhammad Asif Ali, and Di Wang. 2024. A hopfieldian view-based interpretation for chain-of-thought reasoning. arXiv preprint arXiv:2406.12255

    2024 arXiv

  22. [30]

    Wenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, and Zhiguo Wang. 2024. http://arxiv.org/abs/2401.17585 Propagation and pitfalls: Reasoning-based assessment of knowledge editing through counterfactual tasks

    2024 arXiv

  23. [31]

    Baixiang Huang, Canyu Chen, Xiongxiao Xu, Ali Payani, and Kai Shu. 2024. Can knowledge editing really correct hallucinations? arXiv preprint arXiv:2410.16251

    2024 arXiv

  24. [32]

    Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2023. http://arxiv.org/abs/2301.09785 Transformer-patcher: One mistake worth one neuron

    2023 arXiv

  25. [33]

    Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. https://doi.org/10.18653/v1/K17-1034 Zero-shot relation extraction via reading comprehension . In Proceedings of the 21st Conference on Computational Natural Language Learning ( C o NLL 2017) , pages 333--342, Va...

    2017 doi

  26. [34]

    Jun-Yu Ma, Jia-Chen Gu, Zhen-Hua Ling, Quan Liu, and Cong Liu. 2024. http://arxiv.org/abs/2310.10322 Untying the reversal curse via bidirectional language model editing

    2024 arXiv

  27. [35]

    Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 a . Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems, 35:17359--17372

    2022

  28. [36]

    Kevin Meng, Arnab Sen Sharma, Alex J Andonian, Yonatan Belinkov, and David Bau. 2022 b . Mass-editing memory in a transformer. In The Eleventh International Conference on Learning Representations

    2022

  29. [37]

    Manning, and Chelsea Finn

    Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, and Chelsea Finn. 2022. https://api.semanticscholar.org/CorpusID:249642147 Memory-based model editing at scale . ArXiv, abs/2206.06520

    2022 arXiv

  30. [38]

    Kento Nishi, Maya Okawa, Rahul Ramesh, Mikail Khona, Hidenori Tanaka, and Ekdeep Singh Lubana. 2024. http://arxiv.org/abs/2410.17194 Representation shattering in transformers: A synthetic study with knowledge editing

    2024 arXiv

  31. [39]

    Jingcheng Niu, Andrew Liu, Zining Zhu, and Gerald Penn. 2024. What does the knowledge neuron thesis have to do with knowledge? arXiv preprint arXiv:2405.02421

    2024 arXiv

  32. [40]

    Yasumasa Onoe, Michael J. Q. Zhang, Shankar Padmanabhan, Greg Durrett, and Eunsol Choi. 2023. http://arxiv.org/abs/2305.01651 Can lms learn new entities from descriptions? challenges in propagating injected knowledge

    2023 arXiv

  33. [41]

    Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo, Yixin Cao, Lei Hou, and Juanzi Li. 2024. http://arxiv.org/abs/2402.13093 Event-level knowledge editing

    2024 arXiv

  34. [42]

    Amit Rozner, Barak Battash, Lior Wolf, and Ofir Lindenbaum. 2024. Knowledge editing in language models via adapted direct preference optimization. arXiv preprint arXiv:2406.09920

    2024 arXiv

  35. [43]

    Zhengxiang Shi and Aldo Lipani. 2024. http://arxiv.org/abs/2309.05173 Dept: Decomposed prompt tuning for parameter-efficient fine-tuning

    2024 arXiv

  36. [44]

    Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, and Preslav Nakov. 2023 a . Fake news detectors are biased against texts generated by large language models. arXiv preprint arXiv:2309.08674

    2023 arXiv

  37. [45]

    Jinyan Su, Terry Yue Zhuo, Di Wang, and Preslav Nakov. 2023 b . Detectllm: Leveraging log rank information for zero-shot detection of machine-generated text. arXiv preprint arXiv:2306.05540

    2023 arXiv

  38. [46]

    Yi Su, Jiayi Zhang, Shu Yang, Xinhai Wang, Lijie Hu, and Di Wang. 2025. Understanding how value neurons shape the generation of specified values in llms. arXiv preprint arXiv:2505.17712

    2025 arXiv

  39. [47]

    Alon Talmor and Jonathan Berant. 2018. The web as a knowledge-base for answering complex questions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 641--651

    2018

  40. [48]

    Qwen Team. 2024. https://qwenlm.github.io/blog/qwen2.5/ Qwen2.5: A party of foundation models

    2024

  41. [49]

    Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, et al. 2024. Knowledge mechanisms in large language models: A survey and perspective. arXiv preprint arXiv:2407.15017

    2024 arXiv

  42. [50]

    Peng Wang, Ningyu Zhang, Xin Xie, Yunzhi Yao, Bo Tian, Mengru Wang, Zekun Xi, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, and Huajun Chen. 2023 a . https://api.semanticscholar.org/CorpusID:260887090 Easyedit: An easy-to-use knowledge editing framework for large lan...

    2023 arXiv

  43. [51]

    Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, et al. 2023 b . Knowledge editing for large language models: A survey. arXiv preprint arXiv:2310.16218

    2023 arXiv

  44. [52]

    Weixuan Wang, Barry Haddow, and Alexandra Birch. 2023 c . http://arxiv.org/abs/2312.13040 Retrieval-augmented multilingual knowledge editing

    2023 arXiv

  45. [53]

    Shu Yang, Shenzhe Zhu, Ruoxuan Bao, Liang Liu, Yu Cheng, Lijie Hu, Mengdi Li, and Di Wang. 2024 a . What makes your model a low-empathy or warmth person: Exploring the origins of personality in llms. arXiv preprint arXiv:2410.10863

    2024 arXiv

  46. [54]

    Shu Yang, Shenzhe Zhu, Zeyu Wu, Keyu Wang, Junchi Yao, Junchao Wu, Lijie Hu, Mengdi Li, Derek F Wong, and Di Wang. 2025. Fraud-r1: A multi-round benchmark for assessing the robustness of llm against augmented fraud and phishing inducements. arXiv preprint arXiv:2502.12904

    2025 arXiv

  47. [55]

    Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, and Xueqi Cheng. 2024 b . http://arxiv.org/abs/2402.09656 The butterfly effect of model editing: Few edits can trigger large language models collapse

    2024 arXiv

  48. [56]

    Junchi Yao, Jianhua Xu, Tianyu Xin, Ziyi Wang, Shenzhe Zhu, Shu Yang, and Di Wang. 2025 a . Is your llm-based multi-agent a reliable real-world planner? exploring fraud detection in travel planning. arXiv preprint arXiv:2505.16557

    2025 arXiv

  49. [57]

    Junchi Yao, Shu Yang, Jianhua Xu, Lijie Hu, Mengdi Li, and Di Wang. 2025 b . Understanding the repeat curse in large language models from a feature perspective. arXiv preprint arXiv:2504.14218

    2025

  50. [58]

    Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, and Huajun Chen. 2024. Knowledge circuits in pretrained transformers. arXiv preprint arXiv:2405.17969

    2024 arXiv

  51. [59]

    Xunjian Yin, Jin Jiang, Liming Yang, and Xiaojun Wan. 2023. http://arxiv.org/abs/2312.05497 History matters: Temporal knowledge editing in large language model

    2023 arXiv

  52. [60]

    Lin Zhang, Wenshuo Dong, Zhuoran Zhang, Shu Yang, Lijie Hu, Ninghao Liu, Pan Zhou, and Di Wang. 2025 a . Eap-gp: Mitigating saturation effect in gradient-based automated circuit identification. arXiv preprint arXiv:2502.06852

    2025 arXiv

  53. [61]

    Lin Zhang, Lijie Hu, and Di Wang. 2025 b . Mechanistic unveiling of transformer circuits: Self-influence as a key to model reasoning. arXiv preprint arXiv:2502.09022

    2025 arXiv

  54. [62]

    Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, and Zhumin Chen. 2024 a . http://arxiv.org/abs/2402.13593 Knowledge graph enhanced large language model editing

    2024 arXiv

  55. [63]

    Ningyu Zhang, Yunzhi Yao, Bo Tian, Peng Wang, Shumin Deng, Meng Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, Siyuan Cheng, Ziwen Xu, Xin Xu, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Lei Liang, Zhiqiang Zhang, Xiao-Jun Zhu, Jun Zhou, and Huajun Chen. 2024 b...

    2024 arXiv

  56. [64]

    Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander Smola, and Le Song. 2018. Variational reasoning for question answering with knowledge graph. In Proceedings of the AAAI conference on artificial intelligence, volume 32(1)

    2018

  57. [65]

    Zhuoran Zhang, Yongxiang Li, Zijian Kan, Keyuan Cheng, Lijie Hu, and Di Wang. 2024 c . Locate-then-edit for multi-hop factual recall under knowledge editing. arXiv preprint arXiv:2410.06331

    2024 arXiv

  58. [66]

    Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023 a . https://doi.org/10.18653/v1/2023.emnlp-main.296 Can we edit factual knowledge by in-context learning? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language ...

    2023 doi

  59. [67]

    Ce Zheng, Lei Li, Qingxiu Dong, Yuxuan Fan, Zhiyong Wu, Jingjing Xu, and Baobao Chang. 2023 b . https://api.semanticscholar.org/CorpusID:258832407 Can we edit factual knowledge by in-context learning? ArXiv, abs/2305.12740

    2023 arXiv

  60. [68]

    Zexuan Zhong, Zhengxuan Wu, Christopher D Manning, Christopher Potts, and Danqi Chen. 2023. Mquake: Assessing knowledge editing in language models via multi-hop questions. arXiv preprint arXiv:2305.14795

    2023 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.