Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912959367741440 |
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| author | Upadhyay, Shivani Thakur, Nandan Pradeep, Ronak Craswell, Nick Campos, Daniel Lin, Jimmy |
| author_facet | Upadhyay, Shivani Thakur, Nandan Pradeep, Ronak Craswell, Nick Campos, Daniel Lin, Jimmy |
| contents | The second edition of the TREC Retrieval Augmented Generation (RAG) Track advances research on systems that integrate retrieval and generation to address complex, real-world information needs. Building on the foundation of the inaugural 2024 track, this year's challenge introduces long, multi-sentence narrative queries to better reflect the deep search task with the growing demand for reasoning-driven responses. Participants are tasked with designing pipelines that combine retrieval and generation while ensuring transparency and factual grounding. The track leverages the MS MARCO V2.1 corpus and employs a multi-layered evaluation framework encompassing relevance assessment, response completeness, attribution verification, and agreement analysis. By emphasizing multi-faceted narratives and attribution-rich answers from over 150 submissions this year, the TREC 2025 RAG Track aims to foster innovation in creating trustworthy, context-aware systems for retrieval augmented generation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_09891 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track Upadhyay, Shivani Thakur, Nandan Pradeep, Ronak Craswell, Nick Campos, Daniel Lin, Jimmy Information Retrieval The second edition of the TREC Retrieval Augmented Generation (RAG) Track advances research on systems that integrate retrieval and generation to address complex, real-world information needs. Building on the foundation of the inaugural 2024 track, this year's challenge introduces long, multi-sentence narrative queries to better reflect the deep search task with the growing demand for reasoning-driven responses. Participants are tasked with designing pipelines that combine retrieval and generation while ensuring transparency and factual grounding. The track leverages the MS MARCO V2.1 corpus and employs a multi-layered evaluation framework encompassing relevance assessment, response completeness, attribution verification, and agreement analysis. By emphasizing multi-faceted narratives and attribution-rich answers from over 150 submissions this year, the TREC 2025 RAG Track aims to foster innovation in creating trustworthy, context-aware systems for retrieval augmented generation. |
| title | Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2603.09891 |