通过关系重构提升大模型零样本事实验证能力
ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification
- 利用上下文学习提取主张中实体间关系
- 将证据信息重构为逻辑关联形式，提升推理质量
- 适用于无标注数据的零样本事实核查场景
本文提出ZeFaV——一种基于零样本的事实核查框架，通过利用大语言模型的上下文学习能力，提取主张中实体间的语义关系，将证据信息以关系逻辑形式重新组织，并与原始证据结合生成增强上下文，用于生成事实判断。在多跳事实核查数据集HoVer和FEVEROUS上进行实证实验，结果表明该方法性能可媲美当前最优的验证方法。
原文摘要 · Abstract (English)
In this paper, we propose ZeFaV - a zero-shot based fact-checking verification framework to enhance the performance on fact verification task of large language models by leveraging the in-context learning ability of large language models to extract the relations among the entities within a claim, re-organized the information from the evidence in a relationally logical form, and combine the above information with the original evidence to generate the context from which our fact-checking model provide verdicts for the input claims. We conducted empirical experiments to evaluate our approach on two multi-hop fact-checking datasets including HoVer and FEVEROUS, and achieved potential results results comparable to other state-of-the-art fact verification task methods.
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