QA-Calibration of Language Model Confidence Scores

Putra Manggala, Atalanti A. Mastakouri, Elke Kirschbaum, Shiva Kasiviswanathan, Aaditya Ramdas

Keywords: Calibration, Language Models

TL;DR: Presents a concept of grouped calibration along with corresponding algorithms designed for 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).

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 10170