通过拆解与改写伪证，提升对抗性事实核查的准确率
SUCEA: Reasoning-Intensive Retrieval for Adversarial Fact-checking through Claim Decomposition and Editing
- 将复杂伪证拆分为独立子句，逐个检索证据并优化表述
- 在两个数据集上检索与判断准确率均显著超越基线方法
- 无需训练即可增强现有系统，适合对抗性信息检测场景
自动事实核查因应对虚假信息而受到越来越多关注。尽管取得显著进展，基于检索增强的语言模型仍难以应对人为精心设计的对抗性声明。为此，我们提出一种无需训练的方法，通过重述原始声明以更易找到支持证据。我们的模块化框架SUCEA包含三个步骤：1）声明分段与去上下文化，将对抗性声明拆分为独立子声明；2）迭代式证据检索与声明编辑，根据检索到的证据反复优化子声明；3）证据聚合与标签预测，整合所有检索证据并输出蕴含标签。在两个具有挑战性的事实核查数据集上的实验表明，该框架在检索和蕴含标签准确率上均有显著提升，优于四种强基线方法。
原文摘要 · Abstract (English)
Automatic fact-checking has recently received more attention as a means of combating misinformation. Despite significant advancements, fact-checking systems based on retrieval-augmented language models still struggle to tackle adversarial claims, which are intentionally designed by humans to challenge fact-checking systems. To address these challenges, we propose a training-free method designed to rephrase the original claim, making it easier to locate supporting evidence. Our modular framework, SUCEA, decomposes the task into three steps: 1) Claim Segmentation and Decontextualization that segments adversarial claims into independent sub-claims; 2) Iterative Evidence Retrieval and Claim Editing that iteratively retrieves evidence and edits the subclaim based on the retrieved evidence; 3) Evidence Aggregation and Label Prediction that aggregates all retrieved evidence and predicts the entailment label. Experiments on two challenging fact-checking datasets demonstrate that our framework significantly improves on both retrieval and entailment label accuracy, outperforming four strong claim-decomposition-based baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。