Announcements
TREC RAG is returning for 2026! 🥳
We will share updates on the guidelines and timeline for this year's track soon.
In the meantime:
- Please register for TREC. Under Schedule, use the first bullet to register your organization in Evalbase.
- Join our Google Groups mailing list and Discord! For Google Groups, please include "TREC RAG" in your request to join. Contact njedidi@uwaterloo.ca if you have any issues joining.
More coming! Stay tuned.
Best regards,
TREC RAG Organizers
Overview
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.
The TREC RAG Track aims to bring the research community together around a unified benchmark to evaluate the end-to-end performance of systems that combine retrieval and generation. By structuring participation through distinct but complementary tasks, the track enabled deeper analysis of individual system components and their interactions.
Timeline
- Corpus released: Soon!
- Test topics released: TBD
- Baselines released: TBD
- Submission deadline: TBD
- Results and judgments returned to participants: TBD
- TREC 2026 Conference: November 2026
Organizers of TREC 2026 RAG Track
- Nour Jedidi, University of Waterloo
- Lingwei Gu, University of Waterloo
- Daniel Campos, Zipf AI
- Nandan Thakur, University of Waterloo
- Nick Craswell, Microsoft
- Ronak Pradeep, University of Waterloo
- Shivani Upadhyay, University of Waterloo
- Jimmy Lin, University of Waterloo