Summary of Understanding the Collapse Of Llms in Model Editing, by Wanli Yang et al.
Understanding the Collapse of LLMs in Model Editing
by Wanli Yang, Fei Sun, Jiajun Tan, Xinyu Ma, Du Su, Dawei Yin, Huawei Shen
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed paper investigates the root causes of model collapse in large language models (LLMs) when applying model editing methods, specifically ROME. It identifies two primary factors contributing to this issue: inconsistent handling of prefixed and unprefixed keys in the parameter update equation and the subject of collapse cases usually being the first token with an unusual key distribution. To address this problem, the authors propose a simple yet effective solution: uniformly using prefixed keys during editing and adding prefixes during testing to ensure consistency between training and testing. The experimental results demonstrate that this approach can prevent model collapse while maintaining the effectiveness of edits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand why some language models “collapse” when we try to change them, which is a big problem in making artificial intelligence more useful. It finds out what makes it happen and proposes a simple solution to fix it. By using this solution, we can make language models better at understanding human language without breaking them. |
Keywords
» Artificial intelligence » Token
