Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

Xinran Zhao, Hongming Zhang, Xiaoman Pan +4

For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by…