PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915408882171904 |
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| author | Kachuee, Mohammad Gollapudi, Teja Kim, Minseok Huang, Yin Sun, Kai Yang, Xiao Wang, Jiaqi Shah, Nirav Liu, Yue Colak, Aaron Kumar, Anuj Yih, Wen-tau Dong, Xin Luna |
| author_facet | Kachuee, Mohammad Gollapudi, Teja Kim, Minseok Huang, Yin Sun, Kai Yang, Xiao Wang, Jiaqi Shah, Nirav Liu, Yue Colak, Aaron Kumar, Anuj Yih, Wen-tau Dong, Xin Luna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18857 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning Kachuee, Mohammad Gollapudi, Teja Kim, Minseok Huang, Yin Sun, Kai Yang, Xiao Wang, Jiaqi Shah, Nirav Liu, Yue Colak, Aaron Kumar, Anuj Yih, Wen-tau Dong, Xin Luna Computation and Language Artificial Intelligence Machine Learning 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. |
| title | PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.18857 |