提出新方法实时估计大模型生成文本的不确定性，提升可靠性。
Estimating LLM Uncertainty with Evidence
- 基于对证据强度的建模，解耦计算每个词元的不确定性。
- 在多个数据集上显著优于传统概率方法，识别不可靠词元准确率更高。
- 无需多次采样，适合需要快速判断可靠性的实际应用。
近年来，大型语言模型（LLMs）快速发展并广泛应用于各个领域。然而，当模型缺乏相关知识时，容易产生幻觉，生成不可靠内容。为识别潜在幻觉，已有不确定性估计方法被提出，多数研究证实可靠性与关键词元有关。但现有基于概率的方法在识别词元可靠性方面表现不佳，限制了其实际应用。本文揭示，概率方法失败的原因在于训练阶段积累的证据强度信息丢失。为此，我们提出日志诱导词元不确定性（LogTokU）框架，通过证据建模实现解耦的词元不确定性估计，支持实时推理且无需多次采样。利用估计的不确定性指导下游任务，实验结果表明，LogTokU具有显著效果和应用前景。
原文摘要 · Abstract (English)
Over the past few years, Large Language Models (LLMs) have developed rapidly and are widely applied in various domains. However, LLMs face the issue of hallucinations, generating responses that may be unreliable when the models lack relevant knowledge. To be aware of potential hallucinations, uncertainty estimation methods have been introduced, and most of them have confirmed that reliability lies in critical tokens. However, probability-based methods perform poorly in identifying token reliability, limiting their practical utility. In this paper, we reveal that the probability-based method fails to estimate token reliability due to the loss of evidence strength information which is accumulated in the training stage. Therefore, we present Logits-induced token uncertainty (LogTokU), a framework for estimating decoupled token uncertainty in LLMs, enabling real-time uncertainty estimation without requiring multiple sampling processes. We employ evidence modeling to implement LogTokU and use the estimated uncertainty to guide downstream tasks. The experimental results demonstrate that LogTokU has significant effectiveness and promise.
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