AI RESEARCH
Interpreting Negation in GPT-2: Layer- and Head-Level Causal Analysis
arXiv CS.CL
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ArXi:2603.12423v1 Announce Type: new Negation remains a persistent challenge for modern language models, often causing reversed meanings or factual errors. In this work, we conduct a causal analysis of how GPT-2 Small internally processes such linguistic transformations. We examine its hidden representations at both the layer and head level. Our analysis is based on a self-curated 12,000-pair dataset of matched affirmative and negated sentences, covering multiple linguistic templates and forms of negation.