TREC 2025 RAG挑战用长叙事查询推动可信生成系统研究
Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track
- 引入多句长文本查询，模拟真实复杂检索需求
- 基于MS MARCO V2.1评估，验证答案完整性与溯源性
- 适合关注可解释生成与事实准确性的研究者
TREC 2025 Retrieval Augmented Generation (RAG) Track 是第二届该主题赛事，旨在推动融合检索与生成的系统研究，以应对现实世界中的复杂信息需求。在2024年首届基础上，今年挑战引入长篇、多句式叙事型查询，更贴近深度搜索任务，并强化对推理驱动回答的需求。参赛者需设计兼具检索与生成能力的端到端流水线，同时确保输出透明且有事实依据。评测使用MS MARCO V2.1语料库，采用多层评估框架，涵盖相关性判断、回答完整性、溯源验证及一致性分析。本年度超过150份提交充分体现了对多维度叙事和高溯源性回答的重视，致力于促进可信赖、上下文感知的RAG系统创新。
原文摘要 · Abstract (English)
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.
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