@inproceedings{kachuee-etal-2025-prismrag,
    title = "{P}rism{RAG}: Boosting {RAG} Factuality with Distractor Resilience and Strategized Reasoning",
    author = "Kachuee, Mohammad  and
      Gollapudi, Teja  and
      Kim, Minseok  and
      Huang, Yin  and
      Sun, Kai  and
      Yang, Xiao  and
      Wang, Jiaqi  and
      Shah, Nirav  and
      Liu, Yue  and
      Colak, Aaron  and
      Kumar, Anuj  and
      Yih, Wen-tau  and
      Dong, Xin Luna",
    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.53/",
    pages = "775--798",
    ISBN = "979-8-89176-333-3",
    abstract = "Retrieval-augmented generation (RAG) often falls short when retrieved context includes confusing semi-relevant passages, or when answering questions require deep contextual understanding and reasoning. We propose an efficient fine-tuning framework, called PrismRAG, that (i) trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages, and (ii) instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without relying on extensive human engineered instructions. Evaluated across 12 open-book RAG QA benchmarks spanning diverse application domains and scenarios, PrismRAG improves average factuality by 5.4{\%}, outperforming state-of-the-art solutions. Our method is being deployed in production."
}