提出模型能力校准，让大模型更准确判断自己能否解决任务。
On Calibration of Large Language Models: From Response To Capability
- 用预期正确率替代单次输出正确率，更真实反映模型能力
- 实验表明校准后能更好预测多轮通过率和分配推理资源
- 适合需要可靠置信度评估的部署场景
大型语言模型广泛用于通用问题求解，准确估计置信度对可靠应用至关重要。以往研究主要关注响应级置信度，即单个生成结果的正确性概率。但这一设定与许多实际场景不匹配，因为核心问题是模型整体解决某查询的可能性。我们发现，现代LLM解码的随机性导致单次输出正确性无法反映模型真实能力。为此，提出能力校准，目标是估计模型在特定查询上的期望准确率。形式上区分了能力校准与响应校准，并证明二者在理论上和实证上均不同。建立实证评估框架，测试多种置信度估计方法。结果表明，能力校准后的置信度能显著提升pass@$k$预测精度和推理预算分配效率，为多样化应用奠定基础。
原文摘要 · Abstract (English)
Large language models (LLMs) are widely deployed as general-purpose problem solvers, making accurate confidence estimation critical for reliable use. Prior work on LLM calibration largely focuses on response-level confidence, which estimates the correctness of a single generated output. However, this formulation is misaligned with many practical settings where the central question is how likely a model is to solve a query overall. We show that this mismatch results from the stochastic nature of modern LLM decoding, under which single-response correctness fails to reflect underlying model capability. To address this issue, we introduce capability calibration, which targets the model's expected accuracy on a query. We formally distinguish capability calibration from response calibration and show that the two differ both theoretically and empirically. We establish an empirical evaluation setup and study a range of confidence estimation methods. Our results demonstrate that capability-calibrated confidence improves pass@$k$ prediction and inference budget allocation, establishing a foundation with potential for diverse applications.
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