# Enhancing Health Fact-Checking with LLM-Generated Synthetic Data

arXiv: https://arxiv.org/abs/2508.20525 · HTML: https://mlsanity.com/paper/2508.20525/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical synthetic data and agents, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 8 of its 10 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [Medical synthetic data and agents](https://mlsanity.com/synth/medical-synthetic-data-and-agents/)

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification](https://mlsanity.com/paper/2012.14500/) (2020)
- [Scaling Public Health Text Annotation: Zero-Shot Learning vs. Crowdsourcing for Improved Efficiency and Labeling Accuracy](https://mlsanity.com/paper/2502.06150/) (2025)

## Built on by (most cited first, 3 of 3)

- [Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution](https://mlsanity.com/paper/2603.05308/) (2026)
- [CaseFacts: A Benchmark for Legal Fact-Checking and Precedent Retrieval](https://mlsanity.com/paper/2601.17230/) (2026)
- [When Citations Mislead? A Claim-Level Benchmark for Legal Hallucination Detection](https://mlsanity.com/paper/2610.10971/) (2026)

## Abstract (arXiv)

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.