为大模型生成内容提供细粒度概念级不确定性评估
CLUE: Concept-Level Uncertainty Estimation for Large Language Models
- 将生成文本拆解为独立概念，分别计算每个概念的不确定性
- 相比句子级评估，概念级结果更可解释且能定位错误来源
- 适合需要可信生成的场景，如幻觉检测与故事创作
大型语言模型（LLMs）在自然语言生成任务中表现出色。已有研究指出，其生成过程存在不确定性。然而，现有不确定性估计方法主要关注序列层面，忽略了序列中各个信息单元的差异。这些方法无法对序列中每个成分的不确定性进行独立评估。为此，我们提出一种全新的概念级不确定性估计框架（CLUE）。该框架利用大模型将输出序列转换为概念级表示，将序列分解为独立概念，并分别测量每个概念的不确定性。实验表明，与句子级不确定性相比，CLUE能提供更可解释的评估结果，可有效应用于幻觉检测、故事生成等任务。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable proficiency in various natural language generation (NLG) tasks. Previous studies suggest that LLMs' generation process involves uncertainty. However, existing approaches to uncertainty estimation mainly focus on sequence-level uncertainty, overlooking individual pieces of information within sequences. These methods fall short in separately assessing the uncertainty of each component in a sequence. In response, we propose a novel framework for Concept-Level Uncertainty Estimation (CLUE) for LLMs. We leverage LLMs to convert output sequences into concept-level representations, breaking down sequences into individual concepts and measuring the uncertainty of each concept separately. We conduct experiments to demonstrate that CLUE can provide more interpretable uncertainty estimation results compared with sentence-level uncertainty, and could be a useful tool for various tasks such as hallucination detection and story generation.
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