mlsanityData to 14 Oct 2026

FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation

arXiv 2402.11891 ↗First posted 19 Feb 2024cs.IR cs.CLcited by 19

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 25 of its 57 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

25 of 57 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan

Other references · 24

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  2. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  3. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  4. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  5. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  6. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  7. 2020SPECTER: Document-level Representation Learning using Citation-informed Transformerscited by 430
  8. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 333
  9. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  10. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 318
  11. 2022SGPT: GPT Sentence Embeddings for Semantic Searchcited by 204
  12. 2023SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scalingcited by 172
12 more
  1. 2023Large language models can accurately predict searcher preferencescited by 131
  2. 2023AnglE-optimized Text Embeddingscited by 99
  3. 2023Beyond Yes and No: Improving Zero-Shot LLM Rankers via Scoring Fine-Grained Relevance Labelscited by 86
  4. 2023Perspectives on Large Language Models for Relevance Judgmentcited by 85
  5. 2019A Deep Look into Neural Ranking Models for Information Retrievalcited by 79
  6. 2021Neural ranking models for document retrievalcited by 16
  7. 2024Zero-shot Generative Large Language Models for Systematic Review Screening Automationcited by 14
  8. 2022To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrieverscited by 12
  9. 2023Evaluating Generative Ad Hoc Information Retrievalcited by 11
  10. 2022Lecture Notes on Neural Information Retrievalcited by 7
  11. 2024ReSLLM: Large Language Models are Strong Resource Selectors for Federated Searchcited by 7
  12. 2023Learning To Rank Resources with GNNcited by 6

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Citing papers per year: 2024: 4; 2025: 10; 2026: 5.

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  1. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  2. 2025BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agentcited by 200
  3. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  4. 2024Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agentscited by 35
  5. 2025PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimizationcited by 32
  6. 2024Federated Large Language Models: Current Progress and Future Directionscited by 27
  7. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  8. 2025SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenderscited by 8
  9. 2025Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generationcited by 8
  10. 2025Memory in Large Language Models: Mechanisms, Evaluation and Evolutioncited by 8
  11. 2025Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasetscited by 7
  12. 2025Distributed Retrieval-Augmented Generationcited by 2
7 more
  1. 2026FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapterscited by 2
  2. 2025Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routingcited by 1
  3. 2025Human-Centered Evaluation of RAG outputs: a framework and questionnaire for human-AI collaborationcited by 1
  4. 2026Traceable Cross-Source RAG for Chinese Tibetan Medicine Question Answeringcited by 1
  5. 2026A Compound AI Agent for Conversational Grant Discoverycited by 0
  6. 2026Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generationcited by 0
  7. 2026Federated Agent Optimizationcited by 0

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  1. 2025Federated Retrieval-Augmented Generation: A Systematic Mapping Study
  2. 2024C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System
  3. 2025Benchmarking Deep Search over Heterogeneous Enterprise Data
  4. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems
  5. 2025A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
  6. 2025HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks
  7. 2026DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation
  8. 2024Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems