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What's in a Name? Morphological Shortcuts by LLMs in Pharmacology

arXiv 2606.05616 ↗First posted 4 Jun 2026cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 32 references that are in the map. First posted on arXiv 4 Jun 2026.

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References · 13

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  4. 2023Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representationscited by 209
  5. 2024ReFT: Representation Finetuning for Language Modelscited by 197
  6. 2022CREPE: Open-Domain Question Answering with False Presuppositionscited by 72
  7. 2022(QA)2: Question Answering with Questionable Assumptionscited by 51
  8. 2024MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Modelscited by 21
  9. 2024Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarkscited by 17
  10. 2025Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositionscited by 15
  11. 2022A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Modelscited by 4
  12. 2026Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidencecited by 3
1 more
  1. 2025Community size rather than grammatical complexity better predicts Large Language Model accuracy in a novel Wug Testcited by 2

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