Summary of Identifying Knowledge Editing Types in Large Language Models, by Xiaopeng Li et al.
Identifying Knowledge Editing Types in Large Language Models
by Xiaopeng Li, Shangwen Wang, Shezheng Song, Bin Ji, Huijun Liu, Shasha Li, Jun Ma, Jie Yu
First submitted to arxiv on: 29 Sep 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 A new task, Knowledge Editing Type Identification (KETI), is introduced to identify different types of edits in large language models (LLMs) and prevent malicious misuse. The KETI task aims to provide timely alerts to users when encountering illicit edits that could generate toxic content or misleading actions. Four classical classification models and three BERT-based models are proposed as baseline identifiers for both open-source and closed-source LLMs. Experimental results demonstrate the feasibility of identifying malicious edits in LLMs, with decent identification performance achieved by all seven baseline identifiers. The performance is independent of knowledge editing method reliability and exhibits cross-domain generalization, enabling unknown source edit detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) need to be updated frequently to keep their knowledge up-to-date. This process is called “knowledge editing”. However, this technology can also be used in a bad way, making LLMs generate harmful content or trick people into doing the wrong thing. To stop this from happening, researchers created a new task called Knowledge Editing Type Identification (KETI). KETI helps identify different types of edits made to LLMs and alerts users when an edit is not trustworthy. The team tested several models that can do this job and found that they all work pretty well. This means it’s possible to keep a close eye on what changes are being made to LLMs and prevent them from being used in a harmful way. |
Keywords
» Artificial intelligence » Bert » Classification » Domain generalization
