PreQRAG -- Classify and Rewrite for Enhanced RAG
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911018516480000 |
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| author | Martinez, Damian Riano, Catalina Fang, Hui |
| author_facet | Martinez, Damian Riano, Catalina Fang, Hui |
| contents | This paper presents the submission of the UDInfo team to the SIGIR 2025 LiveRAG Challenge. We introduce PreQRAG, a Retrieval Augmented Generation (RAG) architecture designed to improve retrieval and generation quality through targeted question preprocessing. PreQRAG incorporates a pipeline that first classifies each input question as either single-document or multi-document type. For single-document questions, we employ question rewriting techniques to improve retrieval precision and generation relevance. For multi-document questions, we decompose complex queries into focused sub-questions that can be processed more effectively by downstream components. This classification and rewriting strategy improves the RAG performance. Experimental evaluation of the LiveRAG Challenge dataset demonstrates the effectiveness of our question-type-aware architecture, with PreQRAG achieving the preliminary second place in Session 2 of the LiveRAG challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PreQRAG -- Classify and Rewrite for Enhanced RAG Martinez, Damian Riano, Catalina Fang, Hui Information Retrieval This paper presents the submission of the UDInfo team to the SIGIR 2025 LiveRAG Challenge. We introduce PreQRAG, a Retrieval Augmented Generation (RAG) architecture designed to improve retrieval and generation quality through targeted question preprocessing. PreQRAG incorporates a pipeline that first classifies each input question as either single-document or multi-document type. For single-document questions, we employ question rewriting techniques to improve retrieval precision and generation relevance. For multi-document questions, we decompose complex queries into focused sub-questions that can be processed more effectively by downstream components. This classification and rewriting strategy improves the RAG performance. Experimental evaluation of the LiveRAG Challenge dataset demonstrates the effectiveness of our question-type-aware architecture, with PreQRAG achieving the preliminary second place in Session 2 of the LiveRAG challenge. |
| title | PreQRAG -- Classify and Rewrite for Enhanced RAG |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2506.17493 |