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Overview - Retrieval Augmented Generation (RAG) 2025

Proceedings | Data | Runs | Participants

The (TREC) Retrieval-Augmented Generation Track is intended to foster innovation and research within the field of retrieval-augmented generation systems. This area of research focuses on combining retrieval methods - techniques for finding relevant information within large corpora with Large Language Models (LLMs) to enhance the ability of systems to produce relevant, accurate, updated and contextually appropriate content.

Track coordinator(s):

  • Shivani Upadhyay, University of Waterloo
  • Ronak Pradeep, University of Waterloo
  • Nandan Thakur, University of Waterloo
  • Jimmy Lin, University of Waterloo
  • Nick Craswell, Microsoft

Tasks:

  • trec2025-rag-retrieval: Passage Retrieval
  • trec2025-rag-auggen: Augmented Generation
  • trec2025-rag-generation: Full Retrieval-Augmented Generation
  • trec2025-rag-qrels: Relevance Judgment Generation

Track Web Page: https://trec-rag.github.io/