研究大模型如何内部表征医学文本中的不确定表达，揭示其深层编码机制。
Mapping Clinical Doubt: Locating Linguistic Uncertainty in LLMs
- 构建对比数据集，分析不同语义不确定性的语言特征。
- 发现模型在深层层对不确定性线索有系统性激活响应。
- 成果有助于提升医疗大模型的可解释性与可靠性。
大型语言模型（LLMs）在临床场景中应用日益广泛，对语言不确定性的敏感度可能影响诊断判断与决策。然而，这些模型内部如何表征此类认知不确定性仍不明确。本文关注输入侧的语言不确定性表征，不同于输出置信度量化，研究医学文本中语义模态差异（如‘符合’与‘可能符合’）的内部响应。我们构建了一个对比数据集，并提出层级探针指标模型不确定性敏感度（MSU），用于量化不确定性提示引发的激活变化。结果表明，模型对临床不确定性具有结构化、深度依赖的敏感性，表明认知信息在深层逐步编码。该研究揭示了语言不确定性在大模型中的内在表征方式，为模型可解释性与认知可靠性提供了新视角。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly used in clinical settings, where sensitivity to linguistic uncertainty can influence diagnostic interpretation and decision-making. Yet little is known about where such epistemic cues are internally represented within these models. Distinct from uncertainty quantification, which measures output confidence, this work examines input-side representational sensitivity to linguistic uncertainty in medical text. We curate a contrastive dataset of clinical statements varying in epistemic modality (e.g., 'is consistent with' vs. 'may be consistent with') and propose Model Sensitivity to Uncertainty (MSU), a layerwise probing metric that quantifies activation-level shifts induced by uncertainty cues. Our results show that LLMs exhibit structured, depth-dependent sensitivity to clinical uncertainty, suggesting that epistemic information is progressively encoded in deeper layers. These findings reveal how linguistic uncertainty is internally represented in LLMs, offering insight into their interpretability and epistemic reliability.
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