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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.08505 |
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| _version_ | 1866908644831920128 |
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| author | Koshorek, Omri Granot, Niv Alloni, Aviv Admati, Shahar Hendel, Roee Weiss, Ido Arazi, Alan Cohen, Shay-Nitzan Belinkov, Yonatan |
| author_facet | Koshorek, Omri Granot, Niv Alloni, Aviv Admati, Shahar Hendel, Roee Weiss, Ido Arazi, Alan Cohen, Shay-Nitzan Belinkov, Yonatan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08505 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structured RAG for Answering Aggregative Questions Koshorek, Omri Granot, Niv Alloni, Aviv Admati, Shahar Hendel, Roee Weiss, Ido Arazi, Alan Cohen, Shay-Nitzan Belinkov, Yonatan Computation and Language Machine Learning 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. |
| title | Structured RAG for Answering Aggregative Questions |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2511.08505 |