大模型时代，检索系统需从多智能体视角重新思考查询、文档与排序的互动机制。
A Multi-Agent Perspective on Modern Information Retrieval
- 将查询、文档、排序视为独立智能体，构建动态交互框架
- 实验证明智能体间互动显著影响检索性能
- 适合关注大模型时代检索新范式的研究者
大型语言模型（LLMs）的兴起开启了信息检索（IR）的新时代，如今查询和文档不仅由人类生成，也可由自动化智能体创建。这些智能体可生成查询、撰写文档并执行排序。这一转变挑战了长期存在的IR范式，亟需重新审视理论框架与实践方法。本文主张采用多智能体视角，更准确地捕捉查询智能体、文档智能体与排序智能体之间的复杂交互。通过在多种多智能体检索场景下的实证研究，我们揭示了这些交互对系统性能的显著影响。研究结果强调必须重新评估经典IR范式，并开发新的建模与评估框架以应对现代检索系统的挑战。
原文摘要 · Abstract (English)
The rise of large language models (LLMs) has introduced a new era in information retrieval (IR), where queries and documents that were once assumed to be generated exclusively by humans can now also be created by automated agents. These agents can formulate queries, generate documents, and perform ranking. This shift challenges some long-standing IR paradigms and calls for a reassessment of both theoretical frameworks and practical methodologies. We advocate for a multi-agent perspective to better capture the complex interactions between query agents, document agents, and ranker agents. Through empirical exploration of various multi-agent retrieval settings, we reveal the significant impact of these interactions on system performance. Our findings underscore the need to revisit classical IR paradigms and develop new frameworks for more effective modeling and evaluation of modern retrieval systems.
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