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🔍 Today's pick in Interpretability & Analysis of LMs: The Calibration Gap between Model and Human Confidence in Large Language Models by
@coolprof
H. Tejeda A. Kumar
@Cbelem
@skarny
et al.
This work involves an experimental study to assess human confidence in LLM responses to multiple-choice MMLU questions based on explanations the LLM provides together with the selected answer. The authors experiment with altering the model prompt to reflect the actual prediction confidence in models’ explanations, showing improved calibration for users’ assessment of LLM’s reliability and a better ability to discriminate between correct and incorrect answers. These results suggest the importance of further research on the impact of automatic explanations on users’ perception.
📄 Paper: The Calibration Gap between Model and Human Confidence in Large Language Models (2401.13835)
This work involves an experimental study to assess human confidence in LLM responses to multiple-choice MMLU questions based on explanations the LLM provides together with the selected answer. The authors experiment with altering the model prompt to reflect the actual prediction confidence in models’ explanations, showing improved calibration for users’ assessment of LLM’s reliability and a better ability to discriminate between correct and incorrect answers. These results suggest the importance of further research on the impact of automatic explanations on users’ perception.
📄 Paper: The Calibration Gap between Model and Human Confidence in Large Language Models (2401.13835)