提出单一语义特征提取框架，让医学语言模型解释更稳定可信。
A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models
- 通过构建单义嵌入空间，融合归因与机制解释方法。
- 降低不同解释方法间差异，获得稳定输入重要性评分。
- 适合临床神经科学中阿尔茨海默病早期诊断的可信赖分析。
在阿尔茨海默病进展诊断等临床场景中，语言模型的可解释性仍是关键挑战，早期且可信的预测至关重要。现有归因方法因Transformer模型表示的多义性，导致解释结果高度依赖方法且不稳定；而机制解释方法缺乏与输入输出的直接对齐，也未提供明确重要性分数。本文提出统一的可解释性框架，通过在Transformer层级构建单义嵌入空间，并优化以显式减少方法间差异，生成稳定的输入级重要性评分，同时通过目标层的解压缩表示突出显著特征，推动语言模型在认知健康与神经退行性疾病中的安全可信应用。
原文摘要 · Abstract (English)
Interpretability remains a key challenge for deploying language models (LM) in clinical settings such as progression diagnosis of Alzheimer disease, where early and trustworthy predictions are essential. Existing attribution methods exhibit high inter-method variability and unstable explanations due to the polysemantic nature of Transformer-Based LM and LLM representations, while mechanistic interpretability approaches lack direct alignment with model inputs and outputs and do not provide explicit importance scores. We introduce a unified interpretability framework that integrates attributional and mechanistic perspectives through monosemantic feature extraction. By constructing a monosemantic embedding space at the level of an transformer-based LM layer and optimizing the framework to explicitly reduce inter-method variability, our approach produces stable input-level importance scores and highlights salient features via a decompressed representation of the layer of interest, advancing the safe and trustworthy application of LMs in cognitive health and neurodegenerative disease.
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