Summary of Do As I Do (safely): Mitigating Task-specific Fine-tuning Risks in Large Language Models, by Francisco Eiras et al.
Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models
by Francisco Eiras, Aleksandar Petrov, Philip H.S. Torr, M. Pawan Kumar, Adel Bibi
First submitted to arxiv on: 12 Jun 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 The proposed research addresses the concern that fine-tuning language models on benign instruction-following data can inadvertently make them more prone to complying with harmful queries. The study shows that task-specific fine-tuning, which trains models on datasets with clear ground truth answers, can enhance model performance on specialized downstream tasks. However, it also reveals that malicious actors can manipulate the structure of task-specific datasets to induce dangerous model behaviors while maintaining reasonable task performance. To mitigate this issue, the researchers propose a novel strategy that mixes in safety data, demonstrating its effectiveness and efficiency in re-establishing safety alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that fine-tuning language models can have unintended consequences. When we train models on benign data, it can make them more likely to follow bad instructions. The good news is that training models on specific tasks with clear answers can help them do better on those tasks. But the bad news is that bad actors can manipulate these task-specific datasets to get models to behave badly while still doing okay on the task. To fix this, we need a new approach that adds safety data in a way that keeps models safe. |
Keywords
» Artificial intelligence » Alignment » Fine tuning
