QA-Calibration of Language Model Confidence Scores

TL;DR

This paper introduces QA-calibration, a new approach to ensure that confidence scores from generative QA systems are reliably calibrated across different question groups, improving decision-making in critical applications.

Contribution

The paper proposes QA-calibration, a generalized calibration notion for QA systems, along with discretized posthoc calibration methods and theoretical guarantees, validated on multiple benchmarks and models.

Findings

QA-calibration improves interpretability of confidence scores.

Discretized calibration schemes achieve distribution-free guarantees.

Validated on multiple QA benchmarks and large language models.

Abstract

To use generative question-and-answering (QA) systems for decision-making and in any critical application, these systems need to provide well-calibrated confidence scores that reflect the correctness of their answers. Existing calibration methods aim to ensure that the confidence score is, *on average*, indicative of the likelihood that the answer is correct. We argue, however, that this standard (average-case) notion of calibration is difficult to interpret for decision-making in generative QA. To address this, we generalize the standard notion of average calibration and introduce QA-calibration, which ensures calibration holds across different question-and-answer groups. We then propose discretized posthoc calibration schemes for achieving QA-calibration. We establish distribution-free guarantees on the performance of this method and validate our method on confidence scores returned by elicitation prompts across multiple QA benchmarks and large language models (LLMs).