@inproceedings{hsueh-etal-2024-editing,
title = "Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models",
author = "Hsueh, Cheng-Hsun and
Huang, Paul Kuo-Ming and
Lin, Tzu-Han and
Liao, Che Wei and
Fang, Hung-Chieh and
Huang, Chao-Wei and
Chen, Yun-Nung",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.550",
doi = "10.18653/v1/2024.findings-emnlp.550",
pages = "9417--9429",
abstract = "Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies have identified side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after editing. Despite these findings, evaluating the pitfalls of knowledge editing often relies on inconsistent metrics and benchmarks, lacking a uniform standard. In response, this survey presents a comprehensive study of these side effects, providing a unified perspective on the challenges of knowledge editing in LLMs by conducting experiments with consistent metrics and benchmarks. Additionally, we review related works and outline potential research directions to address these limitations. Our survey highlights the limitations of current knowledge editing methods, emphasizing the need for a deeper understanding of the inner knowledge structures of LLMs and improved knowledge editing methods. To foster future research, we have released the complementary materials publicly (https://github.com/MiuLab/EditLLM-Survey).",
}
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<abstract>Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies have identified side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after editing. Despite these findings, evaluating the pitfalls of knowledge editing often relies on inconsistent metrics and benchmarks, lacking a uniform standard. In response, this survey presents a comprehensive study of these side effects, providing a unified perspective on the challenges of knowledge editing in LLMs by conducting experiments with consistent metrics and benchmarks. Additionally, we review related works and outline potential research directions to address these limitations. Our survey highlights the limitations of current knowledge editing methods, emphasizing the need for a deeper understanding of the inner knowledge structures of LLMs and improved knowledge editing methods. To foster future research, we have released the complementary materials publicly (https://github.com/MiuLab/EditLLM-Survey).</abstract>
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%0 Conference Proceedings
%T Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models
%A Hsueh, Cheng-Hsun
%A Huang, Paul Kuo-Ming
%A Lin, Tzu-Han
%A Liao, Che Wei
%A Fang, Hung-Chieh
%A Huang, Chao-Wei
%A Chen, Yun-Nung
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F hsueh-etal-2024-editing
%X Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies have identified side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after editing. Despite these findings, evaluating the pitfalls of knowledge editing often relies on inconsistent metrics and benchmarks, lacking a uniform standard. In response, this survey presents a comprehensive study of these side effects, providing a unified perspective on the challenges of knowledge editing in LLMs by conducting experiments with consistent metrics and benchmarks. Additionally, we review related works and outline potential research directions to address these limitations. Our survey highlights the limitations of current knowledge editing methods, emphasizing the need for a deeper understanding of the inner knowledge structures of LLMs and improved knowledge editing methods. To foster future research, we have released the complementary materials publicly (https://github.com/MiuLab/EditLLM-Survey).
%R 10.18653/v1/2024.findings-emnlp.550
%U https://aclanthology.org/2024.findings-emnlp.550
%U https://doi.org/10.18653/v1/2024.findings-emnlp.550
%P 9417-9429
Markdown (Informal)
[Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models](https://aclanthology.org/2024.findings-emnlp.550) (Hsueh et al., Findings 2024)
ACL
- Cheng-Hsun Hsueh, Paul Kuo-Ming Huang, Tzu-Han Lin, Che Wei Liao, Hung-Chieh Fang, Chao-Wei Huang, and Yun-Nung Chen. 2024. Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9417–9429, Miami, Florida, USA. Association for Computational Linguistics.