Confidence Should Be Calibrated More Than One Turn Deep

• URL: http://arxiv.org/abs/2604.05397v1
• Date: Tue, 07 Apr 2026 03:48:33 GMT
• Title: Confidence Should Be Calibrated More Than One Turn Deep
• Abstract summary: Large Language Models (LLMs) are increasingly applied in high-stakes domains such as finance, healthcare, and education.<n>Existing work on confidence estimation and calibration, largely focuses on single-turn settings.<n>We introduce the task of multi-turn calibration to reframe calibration from a static property into a dynamic challenge.
• Score: 46.97189518000442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly applied in high-stakes domains such as finance, healthcare, and education, where reliable multi-turn interactions with users are essential. However, existing work on confidence estimation and calibration, a major approach to building trustworthy LLM systems, largely focuses on single-turn settings and overlooks the risks and potential of multi-turn conversations. In this work, we introduce the task of multi-turn calibration to reframe calibration from a static property into a dynamic challenge central to reliable multi-turn conversation, where calibrating model confidence at each turn conditioned on the conversation history is required. We first reveal the risks of this setting: using Expected Calibration Error at turn T (ECE@T), a new metric that tracks calibration dynamics over turns, we show that user feedback (e.g., persuasion) can degrade multi-turn calibration. To address this, we propose MTCal, which minimises ECE@T via a surrogate calibration target, and further leverage calibrated confidence in ConfChat, a decoding strategy that improves both factuality and consistency of the model response in multi-turn interactions. Extensive experiments demonstrate that MT-Cal achieves outstanding and consistent performance in multi-turn calibration, and ConfChat preserves and even enhances model performance in multi-turn interactions. Our results mark multi-turn calibration as one missing link for scaling LLM calibration toward safe, reliable, and real-world use.

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