@inproceedings{barres-etal-2025-generating,
    title = "From Generating Answers to Building Explanations: Integrating Multi-Round {RAG} and Causal Modeling for Scientific {QA}",
    author = "Barres, Victor  and
      McFate, Clifton James  and
      Kalyanpur, Aditya  and
      Saravanakumar, Kailash Karthik  and
      Moon, Lori  and
      Seifu, Natnael  and
      Bautista-Castillo, Abraham",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-industry.42/",
    pages = "515--522",
    ISBN = "979-8-89176-194-0",
    abstract = "Application of LLMs for complex causal question answering can be stymied by their opacity and propensity for hallucination. Although recent approaches such as Retrieval Augmented Generation and Chain of Thought prompting have improved reliability, we argue current approaches are insufficient and further fail to satisfy key criteria humans use to select and evaluate causal explanations. Inspired by findings from the social sciences, we present an implemented causal QA approach that combines iterative RAG with guidance from a formal model of causation. Our causal model is backed by the Cogent reasoning engine, allowing users to interactively perform counterfactual analysis and refine their answer. Our approach has been integrated into a deployed Collaborative Research Assistant (Cora) and we present a pilot evaluation in the life sciences domain."
}