@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://aclanthology.org/2025.naacl-industry.42/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T From Generating Answers to Building Explanations: Integrating Multi-Round RAG and Causal Modeling for Scientific QA
%A Barres, Victor
%A McFate, Clifton James
%A Kalyanpur, Aditya
%A Saravanakumar, Kailash Karthik
%A Moon, Lori
%A Seifu, Natnael
%A Bautista-Castillo, Abraham
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S 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)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F barres-etal-2025-generating
%X 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.
%R 10.18653/v1/2025.naacl-industry.42
%U https://aclanthology.org/2025.naacl-industry.42/
%U https://doi.org/10.18653/v1/2025.naacl-industry.42
%P 515-522
Markdown (Informal)
[From Generating Answers to Building Explanations: Integrating Multi-Round RAG and Causal Modeling for Scientific QA](https://aclanthology.org/2025.naacl-industry.42/) (Barres et al., NAACL 2025)
ACL