让搜索引擎自动优化检索结果，适配不同AI助手需求。
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization
- 通过迭代反馈优化搜索策略，提升多任务RAG模型的检索质量。
- 在KILT基准上平均超越基线，18种RAG模型均表现更优。
- 适合需要个性化检索支持的多模型AI系统研发者。
本文研究如何设计一个统一的搜索引擎，以服务多个具有不同任务、大语言模型（LLM）和RAG策略的检索增强生成（RAG）代理。提出一种迭代方法：在离线阶段，搜索引擎为各RAG代理生成检索结果，并收集其对文档质量的反馈；利用期望最大化算法，基于这些反馈迭代优化搜索引擎，目标是最大化每个代理的效用函数。此外，将该方法扩展至在线场景，使搜索引擎能根据实时反馈动态调整行为，更好地服务于每个代理。在知识密集型语言任务（KILT）数据集上的实验表明，所提方法在18种RAG模型上平均显著优于基线。结果证明，该方法能有效根据反馈为每个RAG代理实现检索“个性化”。最后，通过全面消融实验验证了方法各环节的有效性。
原文摘要 · Abstract (English)
This paper investigates the design of a unified search engine to serve multiple retrieval-augmented generation (RAG) agents, each with a distinct task, backbone large language model (LLM), and RAG strategy. We introduce an iterative approach where the search engine generates retrieval results for the RAG agents and gathers feedback on the quality of the retrieved documents during an offline phase. This feedback is then used to iteratively optimize the search engine using an expectation-maximization algorithm, with the goal of maximizing each agent's utility function. Additionally, we adapt this to an online setting, allowing the search engine to refine its behavior based on real-time individual agents feedback to better serve the results for each of them. Experiments on datasets from the Knowledge-Intensive Language Tasks (KILT) benchmark demonstrates that our approach significantly on average outperforms baselines across 18 RAG models. We demonstrate that our method effectively ``personalizes'' the retrieval for each RAG agent based on the collected feedback. Finally, we provide a comprehensive ablation study to explore various aspects of our method.
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