Unsupervised Query Routing for Retrieval Augmented Generation

Abstract: Query routing for retrieval-augmented generation aims to assign an input query to the most suitable search engine. Existing works rely heavily on supervised datasets that require extensive manual annotation, resulting in high costs and limited scalability, as well as poor generalization to out-of-distribution scenarios. To address these challenges, we introduce a novel unsupervised method that constructs the "upper-bound" response to evaluate the quality of retrieval-augmented responses. This evaluation enables the decision of the most suitable search engine for a given query. By eliminating manual annotations, our approach can automatically process large-scale real user queries and create training data. We conduct extensive experiments across five datasets, demonstrating that our method significantly enhances scalability and generalization capabilities.

• The paper introduces an unsupervised query routing method for RAG that bypasses extensive annotated data requirements.
• It employs multi-sourced responses as an upper bound to assess retrieval quality using similarity and coherence metrics across diverse datasets.
• Empirical results demonstrate consistent performance improvements with more data, proving the method's scalability and generalizability in real-world applications.

Unsupervised Query Routing for Retrieval Augmented Generation

The paper "Unsupervised Query Routing for Retrieval Augmented Generation" addresses the significant challenges in query routing, particularly within the context of Retrieval Augmented Generation (RAG). Traditionally, query routing has relied heavily on supervised methods that require extensive annotated datasets, often limiting scalability and generalization to real-world queries. This paper introduces an innovative unsupervised query routing method that significantly alleviates the dependency on such labor-intensive data. The approach focuses on leveraging "upper-bound" responses to evaluate the quality of retrieval-augmented outputs without using manual annotations. This unsupervised strategy aligns queries with the most appropriate search engines, optimizing retrieval performance and operational efficiency.

The paper provides a comprehensive framework for this approach, beginning with the establishment of single-sourced responses from individual search engines for a particular query. The key innovation lies in constructing multi-sourced responses by aggregating retrievals from all available engines. These multi-sourced responses serve as an upper bound to gauge the quality of single-sourced outputs. A pivotal aspect of the method is the assessment of response quality through metrics like similarity and coherence, facilitating automatic training label generation for the routing model.

A notable demonstration of the methodology's efficacy is the empirical evaluation conducted across five diverse datasets. The results highlight the method’s robustness in scalability and its remarkable ability to generalize across datasets with varying distributions. Notably, the approach consistently improves performance with increased data, demonstrating its capacity to utilize large volumes of real queries effectively. For instance, the paper details experiments that reveal consistent performance improvements across different LLMs, such as Qwen2-max and GPT4, reinforcing the method's applicability in diverse scenarios.

The implications of this research are multifaceted. Practically, the reduced dependency on annotated datasets significantly enhances the feasibility of deploying query routing in dynamic, real-world environments where data diversity is a critical factor. Theoretically, it challenges the existing paradigms of reliance on supervised methods in RAG, presenting a scalable alternative that leverages the inherent strengths of multiple search engines.

Speculatively, future developments in AI could see the integration of similar unsupervised methods across other domains where annotation is currently a bottleneck. The framework presented in this paper could inspire advancements in adaptive learning systems, where decision-making processes are optimized without exhaustive pre-labeled data.

In conclusion, this paper provides a novel perspective on unsupervised learning in query routing for RAG. By introducing a method that intelligently utilizes multi-sourced responses as benchmarks, it opens avenues for scalable, cost-effective improvements in retrieval systems. This work is poised to influence both practical deployments and theoretical understandings of resource-efficient data retrieval in an increasingly AI-driven landscape.