Summary of Dissecting Fine-tuning Unlearning in Large Language Models, by Yihuai Hong et al.
Dissecting Fine-Tuning Unlearning in Large Language Models
by Yihuai Hong, Yuelin Zou, Lijie Hu, Ziqian Zeng, Di Wang, Haiqin Yang
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 Fine-tuning-based unlearning methods have been touted as a means to prevent large language models from recalling targeted harmful, sensitive, or copyrighted information while preserving overall capabilities. However, the effectiveness of these methods is unclear. This work investigates the limitations of fine-tuning-based unlearning through activation patching and parameter restoration experiments. The findings reveal that these methods alter the model’s knowledge retrieval process, suggesting they do not genuinely erase problematic knowledge embedded in the model parameters. Instead, coefficients generated by MLP components in the final layer play a crucial role in controlling model behaviors. Behavioral tests demonstrate that this unlearning mechanism impacts the global behavior of models, affecting unrelated knowledge or capabilities. The study contributes to our understanding of the limitations of fine-tuning-based unlearning methods and highlights the need for alternative solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores ways to “unlearn” certain information in large language models so they don’t recall harmful or sensitive things. The current method being tested, called fine-tuning, isn’t very effective at getting rid of this unwanted knowledge. The study found that fine-tuning changes how the model retrieves information and doesn’t really erase the problematic knowledge. Instead, it uses special coefficients to control what the model says. This means that even if the model is unlearning certain things, it can still affect its overall behavior and recall unrelated information. |
Keywords
» Artificial intelligence » Fine tuning » Recall
