@inproceedings{sankararaman-etal-2024-provenance,
    title = "Provenance: A Light-weight Fact-checker for Retrieval Augmented {LLM} Generation Output",
    author = "Sankararaman, Hithesh  and
      Yasin, Mohammed Nasheed  and
      Sorensen, Tanner  and
      Bari, Alessandro Di  and
      Stolcke, Andreas",
    editor = "Dernoncourt, Franck  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2024",
    address = "Miami, Florida, US",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-industry.97/",
    doi = "10.18653/v1/2024.emnlp-industry.97",
    pages = "1305--1313",
    abstract = "We present a light-weight approach for detecting nonfactual outputs from retrieval-augemented generation (RAG). Given a context and putative output, we compute a factuality score that can be thresholded to yield a binary decision to check the results of LLM-based question-answering, summarization, or other systems. Unlike factuality checkers that themselves rely on LLMs, we use compact, open-source natural language inference (NLI) models that yield a freely accessible solution with low latency and low cost at run-time, and no need for LLM fine-tuning. The approach also enables downstream mitigation and correction of hallucinations, by tracing them back to specific context chunks. Our experiments show high ROC-AUC across a wide range of relevant open source datasets, indicating the effectiveness of our method for fact-checking RAG output."
}