mlsanityData to 7 Oct 2026

AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant

arXiv 2411.06805 ↗First posted 11 Nov 2024cs.CL cs.AI cs.IRcited by 5

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

Abstract (arXiv)

The emergence of Large Language Models (LLMs) has significantly advanced natural language processing, but these models often generate factually incorrect information, known as "hallucination". Initial retrieval-augmented generation (RAG) methods like the "Retrieve-Read" framework was inadequate for complex reasoning tasks. Subsequent prompt-based RAG strategies and Supervised Fine-Tuning (SFT) methods improved performance but required frequent retraining and risked altering foundational LLM capabilities. To cope with these challenges, we propose Assistant-based Retrieval-Augmented Generation (AssistRAG), integrating an intelligent information assistant within LLMs. This assistant manages memory and knowledge through tool usage, action execution, memory building, and plan specification. Using a two-phase training approach, Curriculum Assistant Learning and Reinforced Preference Optimization. AssistRAG enhances information retrieval and decision-making. Experiments show AssistRAG significantly outperforms benchmarks, especially benefiting less advanced LLMs, by providing superior reasoning capabilities and accurate responses.

Key facts. Cited by 5 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 37 of its 41 references that are in the map. First posted on arXiv 11 Nov 2024.

Builds on

37 of 41 references.

References · 37

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  7. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  8. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  9. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  10. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  11. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  12. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
25 more
  1. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  2. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  3. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  4. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  5. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 940
  6. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  7. 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 788
  8. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
  9. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  10. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
  11. 2023Active Retrieval Augmented Generationcited by 506
  12. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  13. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 316
  14. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 279
  15. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 253
  16. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 211
  17. 2020Detecting Hallucinated Content in Conditional Neural Sequence Generationcited by 151
  18. 2024Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Modelscited by 84
  19. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  20. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 63
  21. 2023SAIL: Search-Augmented Instruction Learningcited by 33
  22. 2024Grounding Language Model with Chunking-Free In-Context Retrievalcited by 30
  23. 2024BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidencecited by 28
  24. 2024Metacognitive Retrieval-Augmented Large Language Modelscited by 26
  25. 2023UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Modelscited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 320
  2. 2025Orion-Bix: Bi-Axial Attention for Tabular In-Context Learningcited by 6
  3. 2026Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Modelscited by 3
  4. 2025Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filteringcited by 0
  5. 2026MemoPhishAgent: Memory-Augmented Multi-Modal LLM Agent for Phishing URL Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
  2. 2025ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses
  3. 2024Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models
  4. 2026Adaptive Retrieval helps Reasoning in LLMs -- but mostly if it's not used
  5. 2025RAG-R1: Incentivizing the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
  6. 2025RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization
  7. 2023Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation
  8. 2025R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning