aiqbench analysis
This research exposes a critical safety vulnerability where LLMs rely on morphological affixes to generate plausible but factually incorrect pharmacological properties for fictitious drugs. By localizing this "affix heuristic" behavior to early-mid model layers, the study provides a framework for detecting and mitigating overgeneralization risks in high-stakes medical applications. These findings are essential for developers aiming to align large language models with rigorous domain-specific constraints before deployment.
What's in a Name? Morphological Shortcuts by LLMs in Pharmacology
arXiv:2606.05616v2 Announce Type: replace Abstract: 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.
Title:What's in a Name? Morphological Shortcuts by LLMs in Pharmacology
Abstract: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.
Computation and Language (cs.CL)
arXiv:2606.05616 [cs.CL]
(or arXiv:2606.05616v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.05616 Focus to learn more arXiv-issued DOI via DataCite
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