FactGenius：结合零样本提示与模糊关系挖掘以改进基于知识图谱的事实验证
FactGenius: Combining Zero-Shot Prompting and Fuzzy Relation Mining to Improve Fact Verification with Knowledge Graphs
AI总结：
针对传统事实验核方法依赖人工数据整理、规则化的局限，研究提出FactGenius方法，结合大语言模型零样本提示与知识图谱模糊匹配，经DBpedia优化关联，在FactKG基准上性能优于现有基线，为鲁棒事实验核提供新工具。
摘要：
事实验核是一项关键的自然语言处理（NLP）任务，它通过考量可靠证据来验证主张的真实性。传统方法往往受限于劳动密集型的数据整理和基于规则的方案。本文提出了FactGenius，这是一种将大语言模型（LLM）的零样本提示与知识图谱（KG）上的模糊文本匹配相结合以提升事实验核效果的新方法。FactGenius利用源自维基百科的结构化关联数据集DBpedia，通过相似度度量对LLM生成的关联进行优化以确保准确性。在事实验证基准数据集FactKG上对FactGenius的评估表明，其性能显著优于现有基线，尤其是在微调RoBERTa作为分类器时表现更佳。这种对关联进行过滤和验证的两阶段方法被证明至关重要，它在多种推理类型上都取得了优异性能，也让FactGenius成为实现鲁棒事实验核的一款有前景的工具。相关代码和材料可在https://github.com/SushantGautam/FactGenius 获取。
英文摘要：
Fact-checking is a crucial natural language processing (NLP) task that verifies the truthfulness of claims by considering reliable evidence. Traditional methods are often limited by labour-intensive data curation and rule-based approaches. In this paper, we present FactGenius, a novel method that enhances fact-checking by combining zero-shot prompting of large language models (LLMs) with fuzzy text matching on knowledge graphs (KGs). Leveraging DBpedia, a structured linked data dataset derived from Wikipedia, FactGenius refines LLM-generated connections using similarity measures to ensure accuracy. The evaluation of FactGenius on the FactKG, a benchmark dataset for fact verification, demonstrates that it significantly outperforms existing baselines, particularly when fine-tuning RoBERTa as a classifier. The two-stage approach of filtering and validating connections proves crucial, achieving superior performance across various reasoning types and establishing FactGenius as a promising tool for robust fact-checking. The code and materials are available at https://github.com/SushantGautam/FactGenius.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
