mlsanityData to 7 Oct 2026

Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models

arXiv 2405.00175 ↗First posted 30 Apr 2024cs.CL cs.IRcited by 23

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

Abstract (arXiv)

This paper introduces uRAG--a framework with a unified retrieval engine that serves multiple downstream retrieval-augmented generation (RAG) systems. Each RAG system consumes the retrieval results for a unique purpose, such as open-domain question answering, fact verification, entity linking, and relation extraction. We introduce a generic training guideline that standardizes the communication between the search engine and the downstream RAG systems that engage in optimizing the retrieval model. This lays the groundwork for us to build a large-scale experimentation ecosystem consisting of 18 RAG systems that engage in training and 18 unknown RAG systems that use the uRAG as the new users of the search engine. Using this experimentation ecosystem, we answer a number of fundamental research questions that improve our understanding of promises and challenges in developing search engines for machines.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 44 of its 62 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

44 of 62 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generation21 Apr
  2. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation9 Apr
  3. 2022A Survey on Retrieval-Augmented Text Generation2 Feb

Other references · 41

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  7. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  8. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  9. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  10. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  11. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  12. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
29 more
  1. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
  2. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  3. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  4. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  5. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  6. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  7. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  8. 2019Passage Re-ranking with BERTcited by 629
  9. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 571
  10. 2019ELI5: Long Form Question Answeringcited by 567
  11. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  12. 2017Zero-Shot Relation Extraction via Reading Comprehensioncited by 425
  13. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  14. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 308
  15. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  16. 2018The Natural Language Decathlon: Multitask Learning as Question Answeringcited by 235
  17. 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
  18. 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
  19. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126
  20. 2021KAT: A Knowledge Augmented Transformer for Vision-and-Languagecited by 125
  21. 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
  22. 2018Retrieve and Refine: Improved Sequence Generation Models For Dialoguecited by 82
  23. 2021End-to-End Training of Neural Retrievers for Open-Domain Question Answeringcited by 75
  24. 2021Neural Machine Translation with Monolingual Translation Memorycited by 61
  25. 2022Retrieval-Enhanced Machine Learningcited by 46
  26. 2021UnitedQA: A Hybrid Approach for Open Domain Question Answeringcited by 42
  27. 2020Is Retriever Merely an Approximator of Reader?cited by 30
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  29. 2023Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answeringcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 13; 2026: 5.

23 papers.

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  1. 2025Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learningcited by 39
  2. 2025Parametric Retrieval Augmented Generationcited by 38
  3. 2025PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimizationcited by 29
  4. 2025RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defectscited by 27
  5. 2025JuDGE: Benchmarking Judgment Document Generation for Chinese Legal Systemcited by 18
  6. 2024Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generationcited by 15
  7. 2025LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Sciencecited by 15
  8. 2024Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximizationcited by 14
  9. 2024Accelerating Retrieval-Augmented Generationcited by 14
  10. 2025SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generationcited by 14
  11. 2024Retrieval-Enhanced Machine Learning: Synthesis and Opportunitiescited by 11
  12. 2024Drowning in Documents: Consequences of Scaling Reranker Inferencecited by 8
11 more
  1. 2025The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Paperscited by 6
  2. 2026CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Searchcited by 3
  3. 2025Towards Unification of Hallucination Detection and Fact Verification for Large Language Modelscited by 2
  4. 2026Evaluation of Agents under Simulated AI Marketplace Dynamicscited by 1
  5. 2025Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generationcited by 0
  6. 2025Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Modelscited by 0
  7. 2025LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generationcited by 0
  8. 2025How good are LLMs at Retrieving Documents in a Specific Domain?cited by 0
  9. 2026MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfoliocited by 0
  10. 2026Preference Is Not Intervention: The Structure and Stability Boundaries of Reader-Specific Evidence Utilitycited by 0
  11. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation
  2. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  3. 2024Toward Optimal Search and Retrieval for RAG
  4. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  5. 2024Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
  6. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  7. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  8. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs