用大模型生成假想数据，提升健康信息真假核查效果。
Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
- 用大模型拆解文档为基本事实，构建语句-事实蕴含关系表。
- 合成数据使两个公开数据集的F1得分分别提升0.019和0.049。
- 适合需要增强标注数据的医疗信息核查研究者使用。
健康内容的真伪核查因标注训练数据稀缺而困难。本文提出一种基于大语言模型（LLM）的合成数据生成流程，通过总结源文档、分解为原子事实，并利用LLM构建句子-事实蕴含表，从中生成带二元真伪标签的文本-声明对。这些合成数据与原始数据结合，用于微调基于BERT的核查模型。在PubHealth和SciFact两个公开数据集上的评估显示，相比仅用原始数据训练的模型，本方法分别将F1分数提升了0.019和0.049。结果表明，大模型驱动的合成数据增强能有效提升健康信息核查性能。
原文摘要 · Abstract (English)
Fact-checking for health-related content is challenging due to the limited availability of annotated training data. In this study, we propose a synthetic data generation pipeline that leverages large language models (LLMs) to augment training data for health-related fact checking. In this pipeline, we summarize source documents, decompose the summaries into atomic facts, and use an LLM to construct sentence-fact entailment tables. From the entailment relations in the table, we further generate synthetic text-claim pairs with binary veracity labels. These synthetic data are then combined with the original data to fine-tune a BERT-based fact-checking model. Evaluation on two public datasets, PubHealth and SciFact, shows that our pipeline improved F1 scores by up to 0.019 and 0.049, respectively, compared to models trained only on the original data. These results highlight the effectiveness of LLM-driven synthetic data augmentation in enhancing the performance of health-related fact-checkers.
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