mlsanityData to 7 Oct 2026

PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents

arXiv 2407.09394 ↗First posted 12 Jul 2024cs.IRcited by 28

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

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Abstract (arXiv)

Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG) models address this by enhancing LLMs with external knowledge, but often fail to personalize the retrieval process. This paper introduces PersonaRAG, a novel framework incorporating user-centric agents to adapt retrieval and generation based on real-time user data and interactions. Evaluated across various question answering datasets, PersonaRAG demonstrates superiority over baseline models, providing tailored answers to user needs. The results suggest promising directions for user-adapted information retrieval systems.

Key facts. Cited by 28 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 14 of its 36 references that are in the map. First posted on arXiv 12 Jul 2024.

Builds on

14 of 36 references.

References · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  6. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  7. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  8. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  9. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 571
  10. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  11. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  12. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 253
2 more
  1. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  2. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 13; 2026: 14.

28 papers.

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  1. 2025Deep Research Agents: A Systematic Examination And Roadmapcited by 122
  2. 2025PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memorycited by 82
  3. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrievalcited by 31
  4. 2025A Survey of Personalization: From RAG to Agentcited by 26
  5. 2024On the Way to LLM Personalization: Learning to Remember User Conversationscited by 21
  6. 2025Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrievalcited by 11
  7. 2026Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directionscited by 10
  8. 2025Agentic Workflows for Conversational Human-AI Interaction Designcited by 8
  9. 2025EnronQA: Towards Personalized RAG over Private Documentscited by 8
  10. 2025Reasoning LLMs for User-Aware Multimodal Conversational Agentscited by 4
  11. 2025LEANN: A Low-Storage Vector Indexcited by 4
  12. 2026Your Language Model Secretly Contains Personality Subnetworkscited by 4
16 more
  1. 2026From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agentscited by 3
  2. 2025Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Realitycited by 2
  3. 2025PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLMcited by 2
  4. 2025SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaborationcited by 1
  5. 2026AgentSim: A Platform for Verifiable Agent-Trace Simulationcited by 1
  6. 2026NuggetIndex: Governed Atomic Retrieval for Maintainable RAGcited by 1
  7. 2026From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agentscited by 1
  8. 2026Metadata, Structure, or Strategy? A Decomposition of RAG Context Enrichmentcited by 1
  9. 2026The Compaction Cliff in Long-Running AI Agent Memorycited by 1
  10. 2025Enhancing Critical Thinking with AI: A Tailored Warning System for RAG Modelscited by 0
  11. 2026ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generationcited by 0
  12. 2026From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Userscited by 0
  13. 2026As We May Searchcited by 0
  14. 2026hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advisingcited by 0
  15. 2026PragAlign: Feedback-Guided Pragmatic Alignment for Controlled Synthetic Dialogue Generationcited by 0
  16. 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. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  2. 2024A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data
  3. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  4. 2024Bridging the Preference Gap between Retrievers and LLMs
  5. 2026Is Agentic RAG worth it? An experimental comparison of RAG approaches
  6. 2024DuetRAG: Collaborative Retrieval-Augmented Generation
  7. 2025ImpRAG: Retrieval-Augmented Generation with Implicit Queries
  8. 2024RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation