In this episode, we discuss 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. The paper introduces SaySelf, a framework for training large language models (LLMs) to produce accurate, fine-grained confidence estimates and self-reflective rationales explaining their uncertainties. This is achieved by analyzing inconsistencies in multiple reasoning chains, summarizing uncertainties in natural language, and applying supervised fine-tuning alongside reinforcement learning to calibrate confidence levels. Experimental results show that SaySelf effectively reduces confidence calibration errors and maintains task performance, enhancing LLMs' reliability by mitigating overconfidence in erroneous outputs.
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