Summary of Stealth Edits to Large Language Models, by Oliver J. Sutton et al.
Stealth edits to large language models
by Oliver J. Sutton, Qinghua Zhou, Wei Wang, Desmond J. Higham, Alexander N. Gorban, Alexander Bastounis, Ivan Y. Tyukin
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 abstract proposes a new approach to editing large language models, which can be done without retraining. The authors reveal theoretical foundations of stealth editing methods that update a model’s weights to manipulate its response to specific prompts. A single metric is introduced to assess a model’s editability and predict the success of various editing approaches. The paper also highlights the vulnerability of language models to stealth attacks, which can be used to fix a model’s response to a single prompt. Experimental results support the proposed methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being edited without retraining! Scientists have discovered ways to make changes to these powerful tools without having to start from scratch. They’ve found that by using a special metric, they can predict how well an editing method will work. This helps them avoid mistakes and get better results. The team also warns about the dangers of “stealth attacks” – small changes to a model’s weights that can manipulate its response to specific prompts. |
Keywords
» Artificial intelligence » Prompt
