mlsanityData to 7 Oct 2026

RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation

arXiv 2507.20059 ↗First posted 26 Jul 2025cs.CL cs.AI cs.LGcited by 3

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

Abstract (arXiv)

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved at inference time. While RAG demonstrates strong performance on benchmarks largely derived from general-domain corpora like Wikipedia, its effectiveness under realistic, diverse retrieval scenarios remains underexplored. We evaluated RAG systems using MassiveDS, a large-scale datastore with mixture of knowledge, and identified critical limitations: retrieval mainly benefits smaller models, rerankers add minimal value, and no single retrieval source consistently excels. Moreover, current LLMs struggle to route queries across heterogeneous knowledge sources. These findings highlight the need for adaptive retrieval strategies before deploying RAG in real-world settings. Our code and data can be found at https://github.com/ritaranx/RAG_in_the_Wild.

Key facts. Cited by 3 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 36 of its 46 references that are in the map. First posted on arXiv 26 Jul 2025.

Builds on

36 of 46 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models2 Oct
  2. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan

Other references · 34

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2025Qwen3 Technical Reportcited by 8,195
  4. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  5. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  6. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  7. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  8. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  9. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
  10. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
  11. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  12. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
22 more
  1. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  2. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  3. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  4. 2017Crowdsourcing Multiple Choice Science Questionscited by 742
  5. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 633
  6. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 571
  7. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  8. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 326
  9. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 272
  10. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 258
  11. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 200
  12. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuningcited by 194
  13. 2025ReasonIR: Training Retrievers for Reasoning Taskscited by 76
  14. 2025Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learningcited by 58
  15. 2024Scaling Retrieval-Based Language Models with a Trillion-Token Datastorecited by 54
  16. 2024BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrieverscited by 38
  17. 2023RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Modelscited by 36
  18. 2024CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Masterycited by 30
  19. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
  20. 2025Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applicationscited by 13
  21. 2024Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenariocited by 10
  22. 2025Self-Routing RAG: Binding Selective Retrieval with Knowledge Verbalizationcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Proof-of-Use: Mitigating Tool-Call Hacking in Deep Research Agentscited by 3
  2. 2025Improving Language Agents through BREW: Bootstrapping expeRientially-learned Environmental knoWledgecited by 3
  3. 2026Same Agent, Different Answers: A Repeat-Aware Audit of Corpus-Induced Answer Churn in Retrieval-Augmented QAcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  2. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  4. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  5. 2025Dynamic and Parametric Retrieval-Augmented Generation
  6. 2024Meta Knowledge for Retrieval Augmented Large Language Models
  7. 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity
  8. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey