mlsanityData to 14 Oct 2026

Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization

arXiv 2505.15444 ↗First posted 21 May 2025cs.CL cs.AIcited by 1

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

Abstract (arXiv)

Existing studies have optimized retrieval-augmented generation (RAG) across various sub-tasks, such as query understanding and retrieval refinement, but integrating these optimizations into a unified framework remains challenging. To tackle this problem, this work proposes RoleRAG, a unified RAG framework that achieves efficient multi-task processing through role-specific token optimization. RoleRAG comprises six modules, each handling a specific sub-task within the RAG process. Additionally, we introduce a query graph to represent the decomposition of the query, which can be dynamically resolved according to the decomposing state. All modules are driven by the same underlying LLM, distinguished by task-specific role tokens that are individually optimized. This design allows RoleRAG to dynamically activate different modules within a single LLM instance, thereby streamlining deployment and reducing resource consumption. Experimental results on five open-domain question-answering datasets demonstrate the effectiveness, generalizability, and flexibility of our framework.

Key facts. Cited by 1 paper 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 28 of its 41 references that are in the map. First posted on arXiv 21 May 2025.

Builds on

28 of 41 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline28 Oct
  2. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research22 May

Other references · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2024The Llama 3 Herd of Modelscited by 14,715
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  6. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  7. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  8. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  9. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  10. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  11. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  12. 2022Solving math word problems with process- and outcome-based feedbackcited by 901
14 more
  1. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  2. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
  3. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  4. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  5. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  6. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  7. 2023OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoningcited by 85
  8. 2024MindSearch: Mimicking Human Minds Elicits Deep AI Searchercited by 66
  9. 2024RaFe: Ranking Feedback Improves Query Rewriting for RAGcited by 49
  10. 2017Query expansion techniques for information retrieval: A surveycited by 44
  11. 2024Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMscited by 34
  12. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
  13. 2024BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidencecited by 28
  14. 2024TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generationcited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DynSess: Dynamic Session-Level Evaluation and Optimization Framework for Role-Playing Agentscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
  2. 2025RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets
  3. 2026DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation
  4. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  5. 2024Toward Optimal Search and Retrieval for RAG
  6. 2024RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation
  7. 2025PreQRAG -- Classify and Rewrite for Enhanced RAG
  8. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely