Summary of Course-correction: Safety Alignment Using Synthetic Preferences, by Rongwu Xu et al.
Course-Correction: Safety Alignment Using Synthetic Preferences
by Rongwu Xu, Yishuo Cai, Zhenhong Zhou, Renjie Gu, Haiqin Weng, Yan Liu, Tianwei Zhang, Wei Xu, Han Qiu
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper presents a systematic study on assessing and improving large language models (LLMs) to perform the task of course-correction, which involves autonomously steering away from generating harmful content. The researchers introduce the C2-Eval benchmark for quantitative assessment and analyze 10 popular LLMs, revealing varying proficiency in course-correction among current safety-tuned LLMs. To improve this capability, they propose fine-tuning LLMs with preference learning, emphasizing the preference for timely course-correction. They create a synthetic dataset called C2-Syn to teach models this concept through data-driven preference learning. The experiments on two LLMs, Llama2-Chat 7B and Qwen2 7B, show that their method effectively enhances course-correction skills without affecting general performance. This approach also improves the safety of LLMs, particularly in resisting jailbreak attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure large language models don’t create harmful content. The researchers want to improve these models’ ability to avoid generating bad stuff on their own. They test 10 popular models and find that some are better at this than others. To help models do a better job, they suggest fine-tuning them using preference learning, which teaches the model what is and isn’t acceptable. This approach helps models correct course without making mistakes. |
Keywords
* Artificial intelligence * Fine tuning
