IRCLFeb 10

Overview of the TREC 2025 RAGTIME Track

arXiv:2602.10024v14 citationsh-index: 28
Originality Synthesis-oriented
AI Analysis

This work addresses the problem of evaluating multilingual report generation and retrieval for researchers, but it is incremental as it builds on existing TREC tracks.

The TREC 2025 RAGTIME Track studied report generation from multilingual news documents in Arabic, Chinese, English, and Russian, with 125 runs submitted by 13 teams across three tasks including Multilingual Report Generation, English Report Generation, and Multilingual Information Retrieval.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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