FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
This addresses the need for modern evaluation benchmarks for federated search in RAG systems, which is incremental as it builds on existing datasets like BEIR.
The authors tackled the lack of datasets for evaluating federated search in Retrieval-Augmented Generation (RAG) pipelines by creating FeB4RAG, a novel dataset with 790 conversational queries and LLM-derived relevance judgments, and demonstrated that high-quality federated search improves RAG responses compared to naive approaches.
Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing uptake of Retrieval-Augmented Generation (RAG) pipelines, federated search can play a pivotal role in sourcing relevant information across heterogeneous data sources to generate informed responses. However, existing datasets, such as those developed in the past TREC FedWeb tracks, predate the RAG paradigm shift and lack representation of modern information retrieval challenges. To bridge this gap, we present FeB4RAG, a novel dataset specifically designed for federated search within RAG frameworks. This dataset, derived from 16 sub-collections of the widely used \beir benchmarking collection, includes 790 information requests (akin to conversational queries) tailored for chatbot applications, along with top results returned by each resource and associated LLM-derived relevance judgements. Additionally, to support the need for this collection, we demonstrate the impact on response generation of a high quality federated search system for RAG compared to a naive approach to federated search. We do so by comparing answers generated through the RAG pipeline through a qualitative side-by-side comparison. Our collection fosters and supports the development and evaluation of new federated search methods, especially in the context of RAG pipelines.