[2605.03052] How Language Models Process Negation
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Computer Science > Computation and Language
arXiv:2605.03052 (cs)
[Submitted on 4 May 2026]
Title:How Language Models Process Negation
Authors:Zhejian Zhou, Tianyi Zhou, Robin Jia, Jonathan May
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Abstract:We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong answers to questions involving negation, they do possess internal components that process negation correctly. Their poor accuracy is due to late-layer attention behavior that promotes simple shortcuts; ablating those attention modules greatly improves accuracy on negation-related questions. Second, we uncover how models process negation. We consider two hypotheses: models could use attention heads that attend to the phrase being negated and suppress related concepts, or they could directly construct a representation of the entire negative phrase (e.g., representing "not gas" as a vector that promotes liquids and solids). We apply a range of observational and causal interpretability techniques on Mistral-7B and Llama-3.1-8B to show that models implement both mechanisms, with the "constructive" mechanism being more prominent. Combined, our work deepens the understanding of LLMs' internals, highlighting construction-dominant computations and the coexistence of competing mechanisms within LLMs.
Comments: ICML2026 preprint, camera ready in progress
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.03052 [cs.CL]
(or arXiv:2605.03052v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.03052
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhejian Zhou [view email] [v1] Mon, 4 May 2026 18:17:05 UTC (513 KB)
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