Out of the Black Box: Uncertainty Quantification for LLMs via Conditional Probabilities

Hui Chen, Antoine Didisheim and Luciano A. Somoza

No 34965, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Autoregressive LLMs generate text by sampling from estimated probability distributions over the next token, conditional on prior context. We use these probabilities to construct an entropy-based measure of prediction uncertainty, termed inner confidence. In news classification, LLM predictions with higher inner confidence are systematically more accurate. To evaluate the measure's economic relevance, we form long-short portfolios based on LLM predictions. The portfolio based on high-confidence predictions achieves a Sharpe ratio about 20\% higher than the unconditional benchmark, while the one based on low-confidence predictions yields no excess returns. In contrast, self-declared confidence exhibits significant decoding biases and provides no comparable performance gains.

JEL-codes: C45 C55 G11 G14 (search for similar items in EconPapers)
Date: 2026-03
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