@inproceedings{tran-etal-2025-leaf,
    title = "{LEAF}: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models",
    author = "Tran, Hieu  and
      Wang, Junda  and
      Ting, Yujan  and
      Yu, Hong  and
      Huang, Weijing  and
      Chen, Terrence",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.23/",
    pages = "338--363",
    ISBN = "979-8-89176-333-3",
    abstract = "Large language models (LLMs) often struggle with factual accuracy in knowledge-intensive domains like healthcare. We introduce LEAF (Learning and Evaluation Augmented by Fact-Checking), a framework for improving LLM factuality in medical question answering. LEAF comprises three components: (1) \textbf{RAFE}, a robust fact-checking system using open-source LLMs and domain-specific retrieval to evaluate response accuracy; (2) \textbf{Fact-Check-then-RAG}, which leverages fact-checking results to guide retrieval without parameter updates; and (3) \textbf{Learning from Fact Check}, enabling self-training through supervised fine-tuning or preference-based learning using fact-checking as pseudo-labels. Experimental results show that RAFE outperforms Factcheck-GPT in detecting inaccuracies, Fact-Check-then-RAG effectively corrects errors, and Learning from Fact Check improves performance without labeled data. In a real-world healthcare deployment with proprietary medical documents, LEAF achieved an 83{\%} improvement in factuality scores, demonstrating practical applicability for adapting general-purpose LLMs to organization-specific knowledge. Our framework provides a scalable solution for industrial applications requiring high factual accuracy."
}