提出TAC方法，让大模型的不确定性估计更可靠。
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
- 用事实对齐方式校准原始不确定性分数
- 在少量噪声标注下仍能生成可靠估计
- 适合提升大模型输出可信度的场景
不确定性估计（UE）旨在识别大语言模型（LLM）的幻觉输出以提升其可靠性。然而，现有UE指标在不同配置下表现不稳定，严重限制了应用。本文将此现象定义为代理失效，因多数UE指标源于模型行为而非输出事实正确性。研究发现，当信息量低时，这些指标会失去区分能力。为此，提出后处理校准方法Truth AnChoring（TAC），将原始得分映射为与事实对齐的分数。即使在噪声和少样本监督下，TAC仍可学习出校准良好的不确定性估计，并提供实用校准协议。研究揭示了将启发式UE指标视为真实不确定性的局限性，强调TAC是实现更可靠不确定性估计的必要步骤。代码已开源。
原文摘要 · Abstract (English)
Uncertainty estimation (UE) aims to detect hallucinated outputs of large language models (LLMs) to improve their reliability. However, UE metrics often exhibit unstable performance across configurations, which significantly limits their applicability. In this work, we formalise this phenomenon as proxy failure, since most UE metrics originate from model behaviour, rather than being explicitly grounded in the factual correctness of LLM outputs. With this, we show that UE metrics become non-discriminative precisely in low-information regimes. To alleviate this, we propose Truth AnChoring (TAC), a post-hoc calibration method to remedy UE metrics, by mapping the raw scores to truth-aligned scores. Even with noisy and few-shot supervision, our TAC can support the learning of well-calibrated uncertainty estimates, and presents a practical calibration protocol. Our findings highlight the limitations of treating heuristic UE metrics as direct indicators of truth uncertainty, and position our TAC as a necessary step toward more reliable uncertainty estimation for LLMs. The code repository is available at https://github.com/ponhvoan/TruthAnchor/.
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