Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models

ACL ARR 2025 February Submission6801 Authors

Abstract: Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper uncertainty scores is complicated by the conditional dependency between the generation steps of an autoregressive LLM, because it is hard to model it explicitly. Here, we propose to learn this dependency from attention-based features. In particular, we train a regression model that leverages LLM attention maps, probabilities on the current generation step, and recurrently computed uncertainty scores from previously generated tokens. To mitigate the overfitting due to ``teacher forcing'' in the recurrent features, we also suggest a two-staged training procedure. Our experimental evaluation on ten datasets and three LLMs shows that the proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: Uncertainty quantification, selective generation, large language models,

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 6801