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Revisiting the Systematicity in Negation in the Era of In-Context Learning
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revisiting-the-systematicity-in-negation-in-the-era-of-in-context-learning-b8651811·1 events·first seen 25h agoAliases: Revisiting the Systematicity in Negation in the Era of In-Context Learning
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Revisiting LLM systematicity in negation understanding via in-context learning
A new arXiv preprint analyzes how well large language models handle negation from two angles: behavioral systematicity (whether models correctly recognize negation expressions and scope) and representational systematicity (whether function vectors can be reliably constructed from in-context examples). Results show LLMs partially succeed at negation cue recognition via in-context learning but struggle with scope recognition, with performance varying by output format. Function vectors can be composed for cue extraction but are harder to extract for scope recognition tasks.