---
title: "Model Editing Harms General Abilities of Large Language"
description: "As a remedy, they propose RECT, a regularizer that retains only the update entries with the largest… 47 references, cited by 51 papers here."
authors: ["Jia-Chen Gu", "Hao-Xiang Xu", "Jun-Yu Ma", "Pan Lu", "Zhen-Hua Ling", "Kai-Wei Chang", "Nanyun Peng"]
year: 2024
venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2024.emnlp-main.934/"
doi: "10.18653/v1/2024.emnlp-main.934"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/gu-2024-model-editing-harms-general-abilities/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/gu-2024-model-editing-harms-general-abilities/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/) / Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue

# Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue

**Authors:** [Jia-Chen Gu](https://md.research.elamur.ai/free/en/authors/jia-chen-gu/), Hao-Xiang Xu, Jun-Yu Ma, [Pan Lu](https://md.research.elamur.ai/free/en/authors/pan-lu/), [Zhen-Hua Ling](https://md.research.elamur.ai/free/en/authors/zhen-hua-ling/), [Kai-Wei Chang](https://md.research.elamur.ai/free/en/authors/kai-wei-chang/) and [Nanyun Peng](https://md.research.elamur.ai/free/en/authors/nanyun-peng/) · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.emnlp-main.934/) · [PDF](https://aclanthology.org/2024.emnlp-main.934.pdf) · [DOI 10.18653/v1/2024.emnlp-main.934](https://doi.org/10.18653/v1/2024.emnlp-main.934) · [OpenAlex](https://openalex.org/W4404782881)

**Citations:** 15 citations (OpenAlex, 2026-10-14) · 51 in our library, of the 141,840 papers whose bibliographies we read

## Summary
Model editing updates facts stored in large language models without full retraining. However, it is usually judged only on whether the edit sticks, generalizes to paraphrases and leaves unrelated facts intact. This EMNLP 2024 paper asks whether such edits quietly damage the models' broader capabilities. The authors tested four editing methods (KN, MEND, ROME, MEMIT) on GPT-2 XL, LLaMA-1 (7B) and LLaMA-2 (7B), using edits drawn from the ZsRE dataset. They compared single and sequential editing as well as instance and batch editing. Zero-shot performance was then measured on eight unrelated tasks covering reasoning, NLI, open- and closed-domain QA, dialogue, summarization, NER and sentiment analysis. Across these settings, edits frequently caused severe degradation. The authors attribute this to updates that shift weights too much and overfit the new facts, with the damage compounding over repeated edits. As a remedy, they propose RECT, a regularizer that retains only the update entries with the largest relative weight change and sets the rest to zero. The work shows that editing benchmarks miss important side effects and offers a simple, cheap mitigation.

## Key points
- The study evaluates 4 editing methods on 3 LLMs across 8 downstream tasks, with all task evaluations run zero-shot.
- In one striking case, a single editing pass that touched under 1% of parameters reduced LLaMA-1 (7B) to near-zero scores on every task tested.
- RECT (RElative Change in weighT) keeps the top-40% of entries in the update matrix ΔW and zeroes the remainder. This preserves over 94% of reliability and generalization and even improves locality. A stricter top-20% cutoff begins to hurt editing quality.
- RECT top-40% outperforms both random 40% sparsification and PCA 40% sparsification, and it is cheaper to compute than PCA. It helps preserve performance on summarization and on open- and closed-domain QA.
- Several limitations remain. Sentiment analysis is still hard to protect, and only the ZsRE editing dataset was used. It is also unknown whether RECT holds up under much larger numbers of edits.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) — University, Hefei, China · 26 works · 111,012 citations
- [University of California, Los Angeles](https://md.research.elamur.ai/free/en/universities/university-of-california-los-angeles/) — University, Los Angeles, United States · 54 works · 53,688 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,085 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,888 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 1,344 works

## Cite

Cite as: Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang and Nanyun Peng. 2024. Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{gu2024model,
  title = {{Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue}},
  author = {Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma, Pan Lu, Zhen-Hua Ling, Kai-Wei Chang and Nanyun Peng},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.934},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.934}
}
```

- [← previous MagicPIG: LSH Sampling for Efficient LLM Generation](https://md.research.elamur.ai/free/en/papers/chen-2024-magicpig-lsh-sampling-efficient-llm/)
- [next → MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/kwan-2024-mt-eval-multi-turn-capabilities/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/)

## References

47 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, et al. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.
2. Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021. Editing factual knowledge in language models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pages 6491–6506. Association for… [find](https://scholar.google.com/scholar?q=Nicola+De+Cao%2C+Wilker+Aziz%2C+and+Ivan+Titov%2E+2021%2E+Editing+factual+knowledge+in+language+models%2E+In+Proceedings+of+the+2021+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+EMNLP+2021%2C+V)
3. Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. [Reading wikipedia to answer open-domain questions](https://md.research.elamur.ai/free/en/papers/chen-2017-reading-wikipedia-answer-open-domain/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pages 1870–1879. Asso…
4. Siyuan Cheng, Bozhong Tian, Qingbin Liu, Xi Chen, Yongheng Wang, Huajun Chen, and Ningyu Zhang. 2023. Can we edit multimodal large language models? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023, pages 13877–13888… [find](https://scholar.google.com/scholar?q=Siyuan+Cheng%2C+Bozhong+Tian%2C+Qingbin+Liu%2C+Xi+Chen%2C+Yongheng+Wang%2C+Huajun+Chen%2C+and+Ningyu+Zhang%2E+2023%2E+Can+we+edit+multimodal+large+language+models%3F+In+Proceedings+of+the+2023+Conference+on+Empirical+M)
5. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023. [Palm: Scaling language modeling with pathways](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/). J. Mach. Learn. Res., 24:240:1–240:113.
6. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. [Boolq: Exploring the surprising difficulty of natural yes/no questions](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:…
7. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021. [Training verifiers to solve math word problems](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/). CoRR, abs/2110.14168.
8. Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva. 2024. Evaluating the ripple effects of knowledge editing in language models. Trans. Assoc. Comput. Linguistics, 12:283–298. [find](https://scholar.google.com/scholar?q=Roi+Cohen%2C+Eden+Biran%2C+Ori+Yoran%2C+Amir+Globerson%2C+and+Mor+Geva%2E+2024%2E+Evaluating+the+ripple+effects+of+knowledge+editing+in+language+models%2E+Trans%2E+Assoc%2E+Comput%2E+Linguistics%2C+12%3A283%E2%80%93298%2E)
9. Leyang Cui, Yu Wu, Shujie Liu, Yue Zhang, and Ming Zhou. 2020. [Mutual: A dataset for multi-turn dialogue reasoning](https://md.research.elamur.ai/free/en/papers/cui-2020-mutual-dataset-multi-turn-dialogue/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 1406–1416. Association for Computational Linguistic…
10. Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005. The PASCAL recognising textual entailment challenge. In Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop, MLCW 2005,… [find](https://scholar.google.com/scholar?q=Ido+Dagan%2C+Oren+Glickman%2C+and+Bernardo+Magnini%2E+2005%2E+The+PASCAL+recognising+textual+entailment+challenge%2E+In+Machine+Learning+Challenges%2C+Evaluating+Predictive+Uncertainty%2C+Visual+Object+Classificati)
11. Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022. Knowledge neurons in pretrained transformers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pages 8493–8502.… [find](https://scholar.google.com/scholar?q=Damai+Dai%2C+Li+Dong%2C+Yaru+Hao%2C+Zhifang+Sui%2C+Baobao+Chang%2C+and+Furu+Wei%2E+2022%2E+Knowledge+neurons+in+pretrained+transformers%2E+In+Proceedings+of+the+60th+Annual+Meeting+of+the+Association+for+Computationa)
12. Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau. 2023. Erasing concepts from diffusion models. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023, pages 2426–2436. IEEE. [find](https://scholar.google.com/scholar?q=Rohit+Gandikota%2C+Joanna+Materzynska%2C+Jaden+Fiotto%2DKaufman%2C+and+David+Bau%2E+2023%2E+Erasing+concepts+from+diffusion+models%2E+In+IEEE%2FCVF+International+Conference+on+Computer+Vision%2C+ICCV+2023%2C+Paris%2C+Franc)
13. Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019. SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 70–79, Hong Kong, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Bogdan+Gliwa%2C+Iwona+Mochol%2C+Maciej+Biesek%2C+and+Aleksander+Wawer%2E+2019%2E+SAMSum+corpus%3A+A+human%2Dannotated+dialogue+dataset+for+abstractive+summarization%2E+In+Proceedings+of+the+2nd+Workshop+on+New+Fronti)
14. Akshat Gupta, Anurag Rao, and Gopala Anumanchipalli. 2024. Model editing at scale leads to gradual and catastrophic forgetting. In Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024, pages 15202–15232. Association for Comput… [find](https://scholar.google.com/scholar?q=Akshat+Gupta%2C+Anurag+Rao%2C+and+Gopala+Anumanchipalli%2E+2024%2E+Model+editing+at+scale+leads+to+gradual+and+catastrophic+forgetting%2E+In+Findings+of+the+Association+for+Computational+Linguistics%2C+ACL+2024%2C+)
15. Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun. 2023. Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Proces… [find](https://scholar.google.com/scholar?q=Peter+Hase%2C+Mohit+Bansal%2C+Been+Kim%2C+and+Asma+Ghandeharioun%2E+2023%2E+Does+localization+inform+editing%3F+surprising+differences+in+causality%2Dbased+localization+vs%2E+knowledge+editing+in+language+models%2E+In+)
16. Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2023. [Transformer-patcher: One mistake worth one neuron](https://md.research.elamur.ai/free/en/papers/huang-2023-transformer-patcher-one-mistake-worth/). In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net.
17. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2023. [Survey of hallucination in natural language generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/). ACM Comput. Surv., 55(12):248:1–248:38.
18. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. [find](https://scholar.google.com/scholar?q=Tom+Kwiatkowski%2C+Jennimaria+Palomaki%2C+Olivia+Redfield%2C+Michael+Collins%2C+Ankur+P%2E+Parikh%2C+Chris+Alberti%2C+Danielle+Epstein%2C+Illia+Polosukhin%2C+Jacob+Devlin%2C+Kenton+Lee%2C+Kristina+Toutanova%2C+Llion+Jones%2C+M)
19. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. [Natural questions: a benchmark for question answering research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/). Trans. Assoc. Comput.
20. Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019. [Latent retrieval for weakly supervised open domain question answering](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/). In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28-August 2, 2019, Volume 1: Long Papers, pages 608…
21. Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. Zero-shot relation extraction via reading comprehension. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), Vancouver, Canada, August 3-4, 2017, pages 333–342. Association for Computational L… [find](https://scholar.google.com/scholar?q=Omer+Levy%2C+Minjoon+Seo%2C+Eunsol+Choi%2C+and+Luke+Zettlemoyer%2E+2017%2E+Zero%2Dshot+relation+extraction+via+reading+comprehension%2E+In+Proceedings+of+the+21st+Conference+on+Computational+Natural+Language+Learni)
22. Zhoubo Li, Ningyu Zhang, Yunzhi Yao, Mengru Wang, Xi Chen, and Huajun Chen. 2024. Unveiling the pitfalls of knowledge editing for large language models. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net. [find](https://scholar.google.com/scholar?q=Zhoubo+Li%2C+Ningyu+Zhang%2C+Yunzhi+Yao%2C+Mengru+Wang%2C+Xi+Chen%2C+and+Huajun+Chen%2E+2024%2E+Unveiling+the+pitfalls+of+knowledge+editing+for+large+language+models%2E+In+The+Twelfth+International+Conference+on+Lear)
23. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
24. Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau. 2015. [The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/). In Proceedings of the SIGDIAL 2015 Conference, The 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 2-4 Sep…
25. Jun-Yu Ma, Jia-Chen Gu, Zhen-Hua Ling, Quan Liu, and Cong Liu. 2023. Untying the reversal curse via bidirectional language model editing. CoRR, abs/2310.10322. — [arXiv:2310.10322](https://arxiv.org/abs/2310.10322) [find](https://scholar.google.com/scholar?q=Jun%2DYu+Ma%2C+Jia%2DChen+Gu%2C+Zhen%2DHua+Ling%2C+Quan+Liu%2C+and+Cong+Liu%2E+2023%2E+Untying+the+reversal+curse+via+bidirectional+language+model+editing%2E+CoRR%2C+abs%2F2310%2E10322%2E+%E2%80%94+%5BarXiv%3A2310%2E10322%5D%28https%3A%2F%2Farxiv%2Eorg%2Fa)
26. Shengyu Mao, Ningyu Zhang, Xiaohan Wang, Mengru Wang, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. 2023. Editing personality for llms. CoRR, abs/2310.02168. — [arXiv:2310.02168](https://arxiv.org/abs/2310.02168) [find](https://scholar.google.com/scholar?q=Shengyu+Mao%2C+Ningyu+Zhang%2C+Xiaohan+Wang%2C+Mengru+Wang%2C+Yunzhi+Yao%2C+Yong+Jiang%2C+Pengjun+Xie%2C+Fei+Huang%2C+and+Huajun+Chen%2E+2023%2E+Editing+personality+for+llms%2E+CoRR%2C+abs%2F2310%2E02168%2E+%E2%80%94+%5BarXiv%3A2310%2E02168%5D%28ht)
27. Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. Locating and editing factual associations in GPT. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9,… [find](https://scholar.google.com/scholar?q=Kevin+Meng%2C+David+Bau%2C+Alex+Andonian%2C+and+Yonatan+Belinkov%2E+2022%2E+Locating+and+editing+factual+associations+in+GPT%2E+In+Advances+in+Neural+Information+Processing+Systems+35%3A+Annual+Conference+on+Neural)
28. Kevin Meng, Arnab Sen Sharma, Alex J. Andonian, Yonatan Belinkov, and David Bau. 2023. Mass-editing memory in a transformer. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. [find](https://scholar.google.com/scholar?q=Kevin+Meng%2C+Arnab+Sen+Sharma%2C+Alex+J%2E+Andonian%2C+Yonatan+Belinkov%2C+and+David+Bau%2E+2023%2E+Mass%2Dediting+memory+in+a+transformer%2E+In+The+Eleventh+International+Conference+on+Learning+Representations%2C+ICLR+)
29. Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D. Manning. 2022a. Fast model editing at scale. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net. [find](https://scholar.google.com/scholar?q=Eric+Mitchell%2C+Charles+Lin%2C+Antoine+Bosselut%2C+Chelsea+Finn%2C+and+Christopher+D%2E+Manning%2E+2022a%2E+Fast+model+editing+at+scale%2E+In+The+Tenth+International+Conference+on+Learning+Representations%2C+ICLR+2022)
30. Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, and Chelsea Finn. 2022b. Memory-based model editing at scale. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 158… [find](https://scholar.google.com/scholar?q=Eric+Mitchell%2C+Charles+Lin%2C+Antoine+Bosselut%2C+Christopher+D%2E+Manning%2C+and+Chelsea+Finn%2E+2022b%2E+Memory%2Dbased+model+editing+at+scale%2E+In+International+Conference+on+Machine+Learning%2C+ICML+2022%2C+17%2D23+Ju)
31. Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, and Jianfeng Gao. 2023. [Check your facts and try again: Improving large language models with external knowledge and automated feedback](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/). CoRR, abs/2302.12813.
32. Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023. [Is chatgpt a general-purpose natural language processing task solver?](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/) In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023…
33. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jeffrey+Wu%2C+Rewon+Child%2C+David+Luan%2C+Dario+Amodei%2C+Ilya+Sutskever%2C+et+al%2E+2019%2E+Language+models+are+unsupervised+multitask+learners%2E+OpenAI+blog%2C+1%288%29%3A9%2E)
34. Erik F. Tjong Kim Sang and Fien De Meulder. 2003. [Introduction to the conll-2003 shared task: Language-independent named entity recognition](https://md.research.elamur.ai/free/en/papers/sang-2003-introduction-conll-2003-shared-task/). In Proceedings of the Seventh Conference on Natural Language Learning, CoNLL 2003, Held in cooperation with HLT-NAACL 2003, Edmonton, Canada, May 31 - June 1,…
35. Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry V. Pyrkin, Sergei Popov, and Artem Babenko. 2020. Editable neural networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. [find](https://scholar.google.com/scholar?q=Anton+Sinitsin%2C+Vsevolod+Plokhotnyuk%2C+Dmitry+V%2E+Pyrkin%2C+Sergei+Popov%2C+and+Artem+Babenko%2E+2020%2E+Editable+neural+networks%2E+In+8th+International+Conference+on+Learning+Representations%2C+ICLR+2020%2C+Addis+A)
36. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 20… [find](https://scholar.google.com/scholar?q=Richard+Socher%2C+Alex+Perelygin%2C+Jean+Wu%2C+Jason+Chuang%2C+Christopher+D%2E+Manning%2C+Andrew+Y%2E+Ng%2C+and+Christopher+Potts%2E+2013%2E+Recursive+deep+models+for+semantic+compositionality+over+a+sentiment+treebank%2E)
37. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023a. [Llama: Open and efficient foundation language models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/). CoRR, a…
38. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, et al. 2023b. Llama 2: Open foundation and fine-tuned chat models… [find](https://scholar.google.com/scholar?q=Hugo+Touvron%2C+Louis+Martin%2C+Kevin+Stone%2C+Peter+Albert%2C+Amjad+Almahairi%2C+Yasmine+Babaei%2C+Nikolay+Bashlykov%2C+Soumya+Batra%2C+Prajjwal+Bhargava%2C+Shruti+Bhosale%2C+Dan+Bikel%2C+Lukas+Blecher%2C+Cristian+Canton%2DFe)
39. Peng Wang, Ningyu Zhang, Xin Xie, Yunzhi Yao, Bozhong Tian, Mengru Wang, Zekun Xi, Siyuan Cheng, Kangwei Liu, Guozhou Zheng, and Huajun Chen. 2024a. EasyEdit: An easy-to-use knowledge editing framework for large language models. In Proceedings of the 62nd Annual Meeting of the Association for Comput… [find](https://scholar.google.com/scholar?q=Peng+Wang%2C+Ningyu+Zhang%2C+Xin+Xie%2C+Yunzhi+Yao%2C+Bozhong+Tian%2C+Mengru+Wang%2C+Zekun+Xi%2C+Siyuan+Cheng%2C+Kangwei+Liu%2C+Guozhou+Zheng%2C+and+Huajun+Chen%2E+2024a%2E+EasyEdit%3A+An+easy%2Dto%2Duse+knowledge+editing+framewor)
40. Yiwei Wang, Muhao Chen, Nanyun Peng, and Kai-Wei Chang. 2024b. Deepedit: Knowledge editing as decoding with constraints. CoRR, abs/2401.10471. — [arXiv:2401.10471](https://arxiv.org/abs/2401.10471) [find](https://scholar.google.com/scholar?q=Yiwei+Wang%2C+Muhao+Chen%2C+Nanyun+Peng%2C+and+Kai%2DWei+Chang%2E+2024b%2E+Deepedit%3A+Knowledge+editing+as+decoding+with+constraints%2E+CoRR%2C+abs%2F2401%2E10471%2E+%E2%80%94+%5BarXiv%3A2401%2E10471%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2401%2E10471%29)
41. Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022. Finetuned language models are zero-shot learners. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview… [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Maarten+Bosma%2C+Vincent+Y%2E+Zhao%2C+Kelvin+Guu%2C+Adams+Wei+Yu%2C+Brian+Lester%2C+Nan+Du%2C+Andrew+M%2E+Dai%2C+and+Quoc+V%2E+Le%2E+2022%2E+Finetuned+language+models+are+zero%2Dshot+learners%2E+In+The+Tenth+Internati)
42. Suhang Wu, Minlong Peng, Yue Chen, Jinsong Su, and Mingming Sun. 2023a. Eva-kellm: A new benchmark for evaluating knowledge editing of llms. CoRR, abs/2308.09954. — [arXiv:2308.09954](https://arxiv.org/abs/2308.09954) [find](https://scholar.google.com/scholar?q=Suhang+Wu%2C+Minlong+Peng%2C+Yue+Chen%2C+Jinsong+Su%2C+and+Mingming+Sun%2E+2023a%2E+Eva%2Dkellm%3A+A+new+benchmark+for+evaluating+knowledge+editing+of+llms%2E+CoRR%2C+abs%2F2308%2E09954%2E+%E2%80%94+%5BarXiv%3A2308%2E09954%5D%28https%3A%2F%2Farxiv%2Eor)
43. Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, and Deyi Xiong. 2023b. DEPN: detecting and editing privacy neurons in pretrained language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10,… [find](https://scholar.google.com/scholar?q=Xinwei+Wu%2C+Junzhuo+Li%2C+Minghui+Xu%2C+Weilong+Dong%2C+Shuangzhi+Wu%2C+Chao+Bian%2C+and+Deyi+Xiong%2E+2023b%2E+DEPN%3A+detecting+and+editing+privacy+neurons+in+pretrained+language+models%2E+In+Proceedings+of+the+2023+C)
44. Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023. Editing large language models: Problems, methods, and opportunities. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, Decem… [find](https://scholar.google.com/scholar?q=Yunzhi+Yao%2C+Peng+Wang%2C+Bozhong+Tian%2C+Siyuan+Cheng%2C+Zhoubo+Li%2C+Shumin+Deng%2C+Huajun+Chen%2C+and+Ningyu+Zhang%2E+2023%2E+Editing+large+language+models%3A+Problems%2C+methods%2C+and+opportunities%2E+In+Proceedings+of+t)
45. Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru 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, Xiaowei Zhu, Jun Zhou, and Huajun Chen. 2024. A comprehensive s… [find](https://scholar.google.com/scholar?q=Ningyu+Zhang%2C+Yunzhi+Yao%2C+Bozhong+Tian%2C+Peng+Wang%2C+Shumin+Deng%2C+Mengru+Wang%2C+Zekun+Xi%2C+Shengyu+Mao%2C+Jintian+Zhang%2C+Yuansheng+Ni%2C+Siyuan+Cheng%2C+Ziwen+Xu%2C+Xin+Xu%2C+Jia%2DChen+Gu%2C+Yong+Jiang%2C+Pengjun+Xie%2C+F)
46. 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. 2023. [Siren’s song in the AI ocean: A survey on hallucination in large language models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/). CoRR, abs/2309.01219.
47. Zexuan Zhong, Zhengxuan Wu, Christopher D. Manning, Christopher Potts, and Danqi Chen. 2023. Mquake: Assessing knowledge editing in language models via multi-hop questions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10… [find](https://scholar.google.com/scholar?q=Zexuan+Zhong%2C+Zhengxuan+Wu%2C+Christopher+D%2E+Manning%2C+Christopher+Potts%2C+and+Danqi+Chen%2E+2023%2E+Mquake%3A+Assessing+knowledge+editing+in+language+models+via+multi%2Dhop+questions%2E+In+Proceedings+of+the+2023+)

*Updated 24 October 2026*