Structured RAG for Answering Aggregative Questions

• URL: http://arxiv.org/abs/2511.08505v1
• Date: Wed, 12 Nov 2025 02:02:05 GMT
• Title: Structured RAG for Answering Aggregative Questions
• Authors: Omri Koshorek, Niv Granot, Aviv Alloni, Shahar Admati, Roee Hendel, Ido Weiss, Alan Arazi, Shay-Nitzan Cohen, Yonatan Belinkov,
• Abstract summary: We propose S-RAG, an approach specifically designed for such queries.<n>At ingestion time, S-RAG constructs a structured representation of the corpus.<n>At inference time, it translates natural-language queries into formal queries over said representation.
• Score: 26.336930378574518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) has become the dominant approach for answering questions over large corpora. However, current datasets and methods are highly focused on cases where only a small part of the corpus (usually a few paragraphs) is relevant per query, and fail to capture the rich world of aggregative queries. These require gathering information from a large set of documents and reasoning over them. To address this gap, we propose S-RAG, an approach specifically designed for such queries. At ingestion time, S-RAG constructs a structured representation of the corpus; at inference time, it translates natural-language queries into formal queries over said representation. To validate our approach and promote further research in this area, we introduce two new datasets of aggregative queries: HOTELS and WORLD CUP. Experiments with S-RAG on the newly introduced datasets, as well as on a public benchmark, demonstrate that it substantially outperforms both common RAG systems and long-context LLMs.

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