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Reasoning-Aware Multicalibrated Reliability Predictors for Language Models

Ekaterina Fedorova, Alessandro Morosini, Chara Podimata · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26645 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

一般的に、個々の反応が正しいかどうか、またはより一般的に信頼できるかどうかの有意義な推定を伝達する必要があるモデルフレームプロバイダは、情報のための大規模な言語モデル(LLMs)を信頼する人々がますます信頼性の間違ったものに依存しているように、論理的な回答が正しいか、あるいはより一般的に信頼性のレベルで信頼性の高いかどうかを伝達する必要があります。 理論的幻想(CoT)の文脈では、既存の仕事は、信頼性の予測を平均的にキャンセルする反対的なエラーを示すことができます。 この仕事では、私たちは、アプリケーション関連のトピックトピックに関する推奨的な多重幻想を導入し、アプリケーション関連のトピック

Machine translation (Meta M2M100); the English original follows.

Abstract

As people increasingly rely on large language models (LLMs) for information, model providers need to communicate meaningful estimates of whether individual responses are correct or, more generally, reliable. In the context of hallucinations, existing work uses multicalibration to make such reliability predictions meaningful across application-relevant groups, such as prompt topics. Topic groups alone, however, can conceal systematic errors: within the same topic and confidence level, responses with different chain-of-thought (CoT) properties may exhibit opposing errors that cancel on average. In this work, we introduce reasoning-aware multicalibration, a framework that supplements application-relevant topic groups with groups derived from observable properties of CoT traces. We highlight that, theoretically, groupings based on CoT are "internally important", i.e., groupings for which the multicalibration guarantee is not societally crucial as it is for high-risk topics, but whose inclusion in the multicalibration procedure could easily and significantly improve predictive accuracy of the final reliability predictor. Empirically, we apply our framework to hallucination prediction and unsafe-response detection across a variety of benchmarks, and find that adding CoT groups to topic-based calibration reduces Brier score in both tasks. Interestingly, we find that multicalibrating with respect to CoT groups alone (without any topic information) already improves calibration across topics, suggesting that the predictor's errors depend on how the model reasoned, rather than what it was asked about in the prompt.

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Topics: Reliability (semiconductor), Variety (cybernetics), Computer science, Context (archaeology), Calibration, Machine learning

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