让大模型自动选最优检索器，提升问答准确率
LTRR: Learning To Rank Retrievers for LLMs
- 将检索器选择建模为学习排序问题，动态匹配查询类型
- 在多个评测集上超越单一检索器，正确答案率显著提升
- 适合需要多类型查询支持的复杂问答系统
检索增强生成（RAG）系统通常依赖单一固定检索器，但现有证据表明，没有一个检索器能在所有查询类型上表现最佳。本文提出一种查询路由方法，根据查询内容从多个检索器中动态选择，采用无需训练的启发式规则和可学习的路由模型。我们将路由问题建模为学习排序任务，提出LTRR框架，通过评估各检索器对下游RAG性能的预期贡献进行排序。在包含多种查询类型的问答基准上进行实验，结果显示基于路由的RAG始终优于最强单检索器基线，尤其在使用答案正确性（AC）目标训练且采用成对排序方法时，效果更佳，其中XGBoost表现最优。此外，该方法对分布外查询具有更强泛化能力。结果表明，训练策略与优化指标的选择对有效查询路由至关重要。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs optimally across all query types. In this paper, we explore a query routing approach that dynamically selects from a pool of retrievers based on the query, using both train-free heuristics and learned routing models. We frame routing as a learning-to-rank problem and introduce LTRR, a framework that Learns To Rank Retrievers according to their expected contribution to downstream RAG performance. Through experiments on diverse question-answering benchmarks with controlled variations in query types, we demonstrate that routing-based RAG consistently surpasses the strongest single-retriever baselines. The gains are particularly substantial when training with the Answer Correctness (AC) objective and when using pairwise ranking methods, with XGBoost yielding the best results. Additionally, our approach exhibits stronger generalization to out-of-distribution queries. Overall, our results underscore the critical role of both training strategy and optimization metric choice in effective query routing for RAG systems.
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