Generating Diverse Negations from Affirmative Sentences
In this work, we focus on improving the robustness of LLMs robustness on negated statements by proposingNegVerse, a data augmentation approach capable of generating various types of negations, including verbal, non-verbal, and affixal. We provide new masking rules and propose a filtering mechanism to identify negation cues and remove degenerate examples, producing diverse and in parallel meaningful negated sentences. We experiment with five real-world datasets and that NegVerse outperforms existing methods and generates negations with higher lexical similarity to the original sentences, better syntactic preservation, and greater negation diversity. Our empirical results also highlight that the proposed approach can generate negated sentences without specific guidance on blank placement. However, the resulting sentences exhibit reduced diversity compared to those generated with such guidance.
Nov 19, 2024
