用规则+提示调优生成多样否定句，解决语言模型否定推理数据不足问题。
Generating Diverse Negations from Affirmative Sentences
- 基于句法结构设计掩码规则，结合冻结LLM与提示调优生成否定句
- 生成的否定句在词汇相似度、句法保留和多样性上均优于现有方法
- 适合需要增强模型否定理解能力的研究者与应用开发者
尽管大语言模型在多种任务中表现优异，但在处理否定句时仍存在推理困难。否定在真实场景中至关重要，常用于动词短语、从句等表达负极性，但现有基准主要包含基础否定形式，忽略复杂类型，导致训练数据不足。本文提出NegVerse，通过从肯定句生成多样否定类型（包括动词性、非动词性和词缀性）来填补数据空白。基于句法结构设计掩码规则，利用冻结的基础LLM与提示调优生成否定句，并引入过滤机制识别否定线索、剔除无效样本，生成具有高语义相关性、句法一致性及多样性的有效扰动。实验表明，NegVerse在词汇相似度、句法保持和否定多样性方面均优于现有方法。代码已公开于https://github.com/DarianRodriguez/NegVerse。
原文摘要 · Abstract (English)
Despite the impressive performance of large language models across various tasks, they often struggle with reasoning under negated statements. Negations are important in real-world applications as they encode negative polarity in verb phrases, clauses, or other expressions. Nevertheless, they are underrepresented in current benchmarks, which mainly include basic negation forms and overlook more complex ones, resulting in insufficient data for training a language model. In this work, we propose NegVerse, a method that tackles the lack of negation datasets by producing a diverse range of negation types from affirmative sentences, including verbal, non-verbal, and affixal forms commonly found in English text. We provide new rules for masking parts of sentences where negations are most likely to occur, based on syntactic structure and use a frozen baseline LLM and prompt tuning to generate negated sentences. We also propose a filtering mechanism to identify negation cues and remove degenerate examples, producing a diverse range of meaningful perturbations. Our results show that NegVerse outperforms existing methods and generates negations with higher lexical similarity to the original sentences, better syntactic preservation and negation diversity. The code is available in https://github.com/DarianRodriguez/NegVerse
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