Summary of Sayself: Teaching Llms to Express Confidence with Self-reflective Rationales, by Tianyang Xu et al.
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales
by Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu, Xingyao Wang, Yangyi Chen, Jing Gao
First submitted to arxiv on: 31 May 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 A new training framework called SaySelf is proposed to improve the confidence estimates of large language models (LLMs) and their ability to identify knowledge gaps. The framework uses an LLM to automatically summarize uncertainties in specific knowledge, which is then used for supervised fine-tuning. Additionally, reinforcement learning with a crafted reward function calibrates the confidence estimates to reduce overconfidence in erroneous outputs. Experimental results demonstrate the effectiveness of SaySelf in reducing calibration error and maintaining task performance. The generated self-reflective rationales are reasonable and can further contribute to calibration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SaySelf is a new way to teach large language models (LLMs) to be more accurate and honest about what they know and don’t know. Right now, LLMs often make mistakes or claim to know things they don’t really understand. SaySelf helps fix this by teaching the models to express their confidence in their answers and explain where they’re not sure. This is important because it makes it easier for us to use these models for real-world applications. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning » Supervised
