提升大模型对话状态追踪的可信度，让系统更懂自己何时可能出错。
Confidence Estimation for LLM-Based Dialogue State Tracking
- 用软最大值、原始得分等方法量化大模型输出置信度。
- 在对话状态追踪任务中，置信度校准效果提升，联合目标准确率更高。
- 适合关注大模型可靠性与幻觉控制的研究者与工程师。
大型语言模型（LLM）生成结果的置信度估计对基于LLM的对话系统至关重要，尤其有助于降低幻觉并防止过度依赖。本文全面探索了针对开放权重和封闭权重模型的置信度估计方法，旨在量化与利用模型不确定性，以提升生成响应的可靠性，重点聚焦于任务导向对话系统（TODS）中的对话状态追踪（DST）。无论模型类型如何，良好的置信度校准对于应对不确定性、提升性能均至关重要。我们评估了四种置信度估计方法：基于softmax、原始词元得分、言语化置信度以及它们的组合，采用曲线下面积（AUC）衡量校准效果，数值越高表示校准越好。此外，我们为封闭模型引入自探查机制进行增强。使用微调后的开放权重模型进行评估，在对话状态追踪任务中实现了更高的联合目标准确率（JGA）。研究还表明，微调开放权重LLM可显著提升AUC表现，说明置信度评分校准更优。
原文摘要 · Abstract (English)
Estimation of a model's confidence on its outputs is critical for Conversational AI systems based on large language models (LLMs), especially for reducing hallucination and preventing over-reliance. In this work, we provide an exhaustive exploration of methods, including approaches proposed for open- and closed-weight LLMs, aimed at quantifying and leveraging model uncertainty to improve the reliability of LLM-generated responses, specifically focusing on dialogue state tracking (DST) in task-oriented dialogue systems (TODS). Regardless of the model type, well-calibrated confidence scores are essential to handle uncertainties, thereby improving model performance. We evaluate four methods for estimating confidence scores based on softmax, raw token scores, verbalized confidences, and a combination of these methods, using the area under the curve (AUC) metric to assess calibration, with higher AUC indicating better calibration. We also enhance these with a self-probing mechanism, proposed for closed models. Furthermore, we assess these methods using an open-weight model fine-tuned for the task of DST, achieving superior joint goal accuracy (JGA). Our findings also suggest that fine-tuning open-weight LLMs can result in enhanced AUC performance, indicating better confidence score calibration.
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