Evaluating the Reliability of Language Models

The evaluation of language models (LLMs) often focuses on parameters such as prediction accuracy and precision. A new study proposes a different approach, analyzing the confidence calibration of models, i.e., how well their certainty estimates align with the actual correctness of the answers.

A New Probing Framework

The proposed framework considers three aspects of confidence: intrinsic, structural consistency, and semantic grounding. The analysis was conducted on ten causal models and six masked models, revealing a general tendency towards overconfidence, especially in the latter.

Implications for LLM Development

The results suggest that even the largest models struggle to accurately encode the semantics of confidence expressions in language. Improving confidence calibration could lead to more reliable and interpretable artificial intelligence systems.