Lexical Redefinition Reveals Context-Sensitive Instruction Following
cs.CL
Submitted: 2026-10-12
Updated: 2026-10-12
Comments: Accepted at the 3rd Workshop on Language Understanding in the Human-Machine Era (LUHME 2026), co-located with EMNLP 2026
License: http://creativecommons.org/licenses/by/4.0/
The gist: How do language models reconcile conflicting contextual evidence within a prompt? We study this question using lexical redefinition, where an explicit instruction specifies how a word should be
Abstract
How do language models reconcile conflicting contextual evidence within a prompt? We study this question using lexical redefinition, where an explicit instruction specifies how a word should be interpreted and a subsequent sentence provides additional evidence through its usage. Across 870 word pairs, we find that lexical redefinitions are not applied as fixed rules. Instead, the behaviour depends systematically on the relationship between the explicit instruction and the intervening sentence: the contextualised usage can preserve, weaken, or completely reverse the instruction. These results suggest that the interpretation of an explicit instruction remains sensitive to subsequent contextual evidence. Lexical redefinition therefore provides a controlled behavioural paradigm for studying how language models integrate multiple sources of information within a prompt.
Sources
- Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs
- Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
- Semantic Anchors in In-Context Learning: Why Small LLMs Cannot Flip Their Labels
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