mlsanityData to 14 Oct 2026

ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization

arXiv 2405.06683 ↗First posted 6 May 2024cs.CLcited by 15

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

Abstract (arXiv)

Retrieval-augmented generation (RAG) for language models significantly improves language understanding systems. The basic retrieval-then-read pipeline of response generation has evolved into a more extended process due to the integration of various components, sometimes even forming loop structures. Despite its advancements in improving response accuracy, challenges like poor retrieval quality for complex questions that require the search of multifaceted semantic information, inefficiencies in knowledge re-retrieval during long-term serving, and lack of personalized responses persist. Motivated by transcending these limitations, we introduce ERAGent, a cutting-edge framework that embodies an advancement in the RAG area. Our contribution is the introduction of the synergistically operated module: Enhanced Question Rewriter and Knowledge Filter, for better retrieval quality. Retrieval Trigger is incorporated to curtail extraneous external knowledge retrieval without sacrificing response quality. ERAGent also personalizes responses by incorporating a learned user profile. The efficiency and personalization characteristics of ERAGent are supported by the Experiential Learner module which makes the AI assistant being capable of expanding its knowledge and modeling user profile incrementally. Rigorous evaluations across six datasets and three question-answering tasks prove ERAGent's superior accuracy, efficiency, and personalization, emphasizing its potential to advance the RAG field and its applicability in practical systems.

Key facts. Cited by 15 papers 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 27 of its 37 references that are in the map. First posted on arXiv 6 May 2024.

Builds on

27 of 37 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  2. 2023RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit8 Jun

Other references · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  8. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  9. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  10. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  11. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  12. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 763
13 more
  1. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 390
  2. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  3. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  4. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  5. 2023L-Eval: Instituting Standardized Evaluation for Long Context Language Modelscited by 233
  6. 2017Constructing Datasets for Multi-hop Reading Comprehension Across Documentscited by 190
  7. 2024A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalistcited by 137
  8. 2023Answering Questions by Meta-Reasoning over Multiple Chains of Thoughtcited by 87
  9. 2021Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Mediacited by 51
  10. 2023Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversationscited by 47
  11. 2023Explaining Legal Concepts with Augmented Large Language Models (GPT-4)cited by 40
  12. 2023Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literaturecited by 29
  13. 2023Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generationcited by 24

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 9; 2026: 1.

15 papers.

Sort:

By area:

  1. 2024Human-inspired Episodic Memory for Infinite Context LLMscited by 62
  2. 2025PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequencecited by 33
  3. 2025PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Datacited by 27
  4. 2025A Survey of Personalization: From RAG to Agentcited by 27
  5. 2024RAP: Retrieval-Augmented Personalization for Multimodal Large Language Modelscited by 24
  6. 2024From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queriescited by 16
  7. 2024Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysiscited by 7
  8. 2025A Survey of LLM × DATAcited by 4
  9. 2024SRSA: A Cost-Efficient Strategy-Router Search Agent for Real-world Human-Machine Interactionscited by 2
  10. 2025Question-Aware Knowledge Graph Prompting for Enhancing Large Language Modelscited by 1
  11. 2025From Deferral to Learning: Online In-Context Knowledge Distillation for LLM Cascadescited by 1
  12. 2025Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detectioncited by 0
3 more
  1. 2025MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Modelscited by 0
  2. 2025Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generationcited by 0
  3. 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. 2024Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
  3. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  4. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  5. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  6. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  7. 2024RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
  8. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices