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arXiv · 2507.18857

PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning

Mohammad Kachuee·Teja Gollapudi·Minseok Kim·Yin Huang·Kai Sun·Xiao Yang·Jiaqi Wang·Nirav Shah·Yue Liu·Aaron Colak·Anuj Kumar·Wen-tau Yih·Xin Luna Dong
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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.

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Mohammad Kachuee, Teja Gollapudi, Minseok Kim, Yin Huang, Kai Sun, Xiao Yang, Jiaqi Wang, Nirav Shah, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong. 2025-07-25. PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning. https://arxiv.org/abs/2507.18857

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