Overview of the TREC 2025 RAGTIME Track
Dawn Lawrie, Sean MacAvaney, James Mayfield, Luca Soldaini, Eugene Yang, Andrew Yates
TL;DR
The RAGTIME track at TREC 2025 evaluates multilingual report generation and information retrieval using a diverse news dataset, with 125 runs from 13 teams across three tasks.
Contribution
This paper introduces the RAGTIME track, its multilingual dataset, tasks, and summarizes the results from participating teams.
Findings
125 runs submitted by 13 teams across three tasks.
The track covers Arabic, Chinese, English, and Russian news stories.
Results provide insights into multilingual report generation and retrieval performance.
Abstract
The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multilingual source documents. The track has created a document collection containing Arabic, Chinese, English, and Russian news stories. RAGTIME includes three task types: Multilingual Report Generation, English Report Generation, and Multilingual Information Retrieval (MLIR). A total of 125 runs were submitted by 13 participating teams (and as baselines by the track coordinators) for three tasks. This overview describes these three tasks and presents the available results.
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