mlsanityData to 14 Oct 2026

RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

arXiv 2408.11381 ↗First posted 21 Aug 2024cs.CLcited by 8

On the map: Retrieval-augmented generation → Conversational and speech RAG

Abstract (arXiv)

Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hallucinations and real-time updating of their knowledge. Current research addresses this bottleneck by equipping LLMs with external knowledge, a technique known as Retrieval Augmented Generation (RAG). However, two key issues constrained the development of RAG. First, there is a growing lack of comprehensive and fair comparisons between novel RAG algorithms. Second, open-source tools such as LlamaIndex and LangChain employ high-level abstractions, which results in a lack of transparency and limits the ability to develop novel algorithms and evaluation metrics. To close this gap, we introduce RAGLAB, a modular and research-oriented open-source library. RAGLAB reproduces 6 existing algorithms and provides a comprehensive ecosystem for investigating RAG algorithms. Leveraging RAGLAB, we conduct a fair comparison of 6 RAG algorithms across 10 benchmarks. With RAGLAB, researchers can efficiently compare the performance of various algorithms and develop novel algorithms.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in Conversational and speech RAG, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 39 references that are in the map. First posted on arXiv 21 Aug 2024.

Builds on

24 of 39 references.

References · 24

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  3. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  6. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  7. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  8. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  9. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  10. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  11. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  12. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
12 more
  1. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  2. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  3. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  4. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 743
  5. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  6. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 476
  7. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  8. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 234
  9. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Researchcited by 166
  10. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  11. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
  12. 2024LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systemscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 2.

8 papers.

  1. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 126
  2. 2025UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generationcited by 6
  3. 2025OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learningcited by 4
  4. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generationcited by 3
  5. 2025TrustRAG: An Information Assistant with Retrieval Augmented Generationcited by 1
  6. 2025Optimizing Retrieval for RAG via Reinforcement Learningcited by 1
  7. 2026RAGe: A Retrieval-Augmented Generation Evaluation Frameworkcited by 0
  8. 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. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  3. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  4. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  5. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  6. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
  7. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
  8. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models