提出集合一致性验证任务，用新模型精准定位多语句逻辑矛盾
Introducing Verification Task of Set Consistency with Set-Consistency Energy Networks
- 构建对比损失框架，学习多条语句间的兼容性
- 能准确识别逻辑矛盾并定位问题语句，性能超越现有方法
- 适合需要保障模型安全可靠的应用场景
检测多个陈述（如句子集合或问答对）之间的逻辑不一致是机器学习中的关键挑战，尤其关乎模型的安全与可靠性。传统基于成对比较的方法难以捕捉需集体评估才能显现的不一致。为此，我们提出集合一致性验证任务，扩展自然语言推理（NLI），评估整个语句集的逻辑连贯性。在此基础上，提出集一致性能量网络（SC-Energy），采用对比损失框架学习语句集合的兼容性。该方法不仅能高效验证不一致并精确定位引发矛盾的具体语句，还显著优于现有基于提示的大型语言模型方法。此外，我们发布了两个新数据集：Set-LConVQA 和 Set-SNLI，用于支持该任务的研究。
原文摘要 · Abstract (English)
Examining logical inconsistencies among multiple statements (such as collections of sentences or question-answer pairs) is a crucial challenge in machine learning, particularly for ensuring the safety and reliability of models. Traditional methods that rely on pairwise comparisons often fail to capture inconsistencies that only emerge when more than two statements are evaluated collectively. To address this gap, we introduce the task of set-consistency verification, an extension of natural language inference (NLI) that assesses the logical coherence of entire sets rather than isolated pairs. Building on this task, we present the Set-Consistency Energy Network (SC-Energy), a novel model that employs a contrastive loss framework to learn the compatibility among a collection of statements. Our approach not only efficiently verifies inconsistencies and pinpoints the specific statements responsible for logical contradictions, but also significantly outperforms existing methods including prompting-based LLM models. Furthermore, we release two new datasets: Set-LConVQA and Set-SNLI for set-consistency verification task.
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