大模型靠药名前缀推断药效，可能误判虚构药物。
What's in a Name? Morphological Shortcuts by LLMs in Pharmacology
- 用真实词根构造假药名，测试模型是否仅凭前缀判断药性。
- 653种药分析显示，模型90%以上依赖前缀推断，易混淆相似前缀药物。
- 早期到中期层的激活模式揭示了这种形貌捷径机制。
单词的形态形式常暗示其意义，但过度依赖此类映射在高风险领域可能导致错误。例如，在医学领域，大模型可仅凭药名前缀（如 wugcillin）自信推断虚构药物的药理特性，并生成看似合理的临床内容。本文通过行为与机制研究，揭示大模型在药理学中的“词缀启发法”现象。利用由真实词根构成的虚构药名，我们发现仅词缀信号即可引发模型对药物类别的整体药理反应。为此提出一个框架，用于判断模型对药物语义的依赖主要来自词缀、词干或整个名称。在653种药物上的应用表明，模型大多通过词缀线索推断药效，却很少明确体现此依赖，甚至会错误关联共享词缀的药物属性。跨模型的激活修补分析进一步将该行为定位至早期到中期网络层。这些发现表明，形态捷径虽隐蔽但可测量，对医疗安全构成实质风险。
原文摘要 · Abstract (English)
The morphological form of a word can often give cues to its meaning, but purely relying on these mappings can lead to overgeneralization in high-stakes domains. In the medical domain, for instance, LLMs can confidently reason about fictitious drugs from their affixes alone (e.g., wugcillin) and generate plausible-looking clinical content. We present a behavioral and mechanistic study of LLM "affix heuristics" in pharmacology. Using fictitious drug names built from real affixes, we show that affix signals alone elicit class-level pharmacological responses. We introduce a framework for identifying whether a model's drug semantics are driven mainly by the affix, the stem, or the drug name as a whole. Applied across 653 drugs, our framework reveals that models often induce drug meaning primarily through affix cues, yet rarely explicitly indicate this reliance, and sometimes incorrectly conflate properties among affix-sharing drugs. Activation patching across models further localizes this behavior to early-mid layers. These findings show that morphological shortcuts pose a subtle but measurable risk to safety.
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