Summary of Should We Really Edit Language Models? on the Evaluation Of Edited Language Models, by Qi Li et al.
Should We Really Edit Language Models? On the Evaluation of Edited Language Models
by Qi Li, Xiang Liu, Zhenheng Tang, Peijie Dong, Zeyu Li, Xinglin Pan, Xiaowen Chu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive evaluation of various model editing methods and their effects on language models. It finds that existing editing methods lead to inevitable performance deterioration on general benchmarks, indicating that they maintain the original model’s abilities within only a few dozen edits. When the number of edits is larger, the intrinsic knowledge structure of the model is disrupted or even completely damaged. Additionally, instruction-tuned models are more robust to editing and show less performance drop on general knowledge after editing. The study also reveals that large-scale language models are more resistant to editing compared to small models. Furthermore, it finds that the safety of edited models is significantly weakened, even for those considered safe. The research highlights the limitations of current editing methods and motivates further exploration into more practical and reliable editing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how to edit knowledge in language models. It looks at many different ways to do this and what happens when you use them. What it found is that most methods make the model worse over time, even if they seem good at first. Some methods are better than others, like using instruction-tuned models or bigger models. But even the best methods have a limit – they can only change things a little bit before making the model broken. This means we need to find new and better ways to edit language models. |
