Reasoning-Aware Multicalibrated Reliability Predictors for Language Models
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 applica...
| Authors | Ekaterina Fedorova, Alessandro Morosini, Chara Podimata |
|---|---|
| Primary category | cs.CY |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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