Making Language Models Robust Against Negation

• URL: http://arxiv.org/abs/2502.07717v1
• Date: Tue, 11 Feb 2025 17:18:47 GMT
• Title: Making Language Models Robust Against Negation
• Authors: MohammadHossein Rezaei, Eduardo Blanco,
• Abstract summary: We propose a self-supervised method to make language models more robust against negation. We show that BERT and RoBERTa further pre-trained on our tasks outperform the off-the-shelf versions on nine negation-related benchmarks.
• Score: 9.818585902859363
• License:
• Abstract: Negation has been a long-standing challenge for language models. Previous studies have shown that they struggle with negation in many natural language understanding tasks. In this work, we propose a self-supervised method to make language models more robust against negation. We introduce a novel task, Next Sentence Polarity Prediction (NSPP), and a variation of the Next Sentence Prediction (NSP) task. We show that BERT and RoBERTa further pre-trained on our tasks outperform the off-the-shelf versions on nine negation-related benchmarks. Most notably, our pre-training tasks yield between 1.8% and 9.1% improvement on CondaQA, a large question-answering corpus requiring reasoning over negation.

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