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Negation is a common linguistic phenomenon. Yet language models face challenges with negation in many natural language understanding tasks such as question answering and natural language inference. In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i.e., paraphrases without negation) to make models more robust against negation. Crucially, our affirmative interpretations are obtained automatically. We show improvements with CondaQA, a large corpus requiring reasoning with negation, and five natural language understanding tasks.

ACL 2024

Paraphrasing in Affirmative Terms Improves Negation Understanding

nlu tasks

affirmative interpretations

negation understanding

paraphrasing

question answering

poster

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models

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Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model

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Paraphrasing in Affirmative Terms Improves Negation Understanding

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