mlsanityData to 7 Oct 2026

A Multi-Source Retrieval Question Answering Framework Based on RAG

arXiv 2405.19207 ↗First posted 29 May 2024cs.IR cs.AIcited by 4

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

With the rapid development of large-scale language models, Retrieval-Augmented Generation (RAG) has been widely adopted. However, existing RAG paradigms are inevitably influenced by erroneous retrieval information, thereby reducing the reliability and correctness of generated results. Therefore, to improve the relevance of retrieval information, this study proposes a method that replaces traditional retrievers with GPT-3.5, leveraging its vast corpus knowledge to generate retrieval information. We also propose a web retrieval based method to implement fine-grained knowledge retrieval, Utilizing the powerful reasoning capability of GPT-3.5 to realize semantic partitioning of problem.In order to mitigate the illusion of GPT retrieval and reduce noise in Web retrieval,we proposes a multi-source retrieval framework, named MSRAG, which combines GPT retrieval with web retrieval. Experiments on multiple knowledge-intensive QA datasets demonstrate that the proposed framework in this study performs better than existing RAG framework in enhancing the overall efficiency and accuracy of QA systems.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 15 of its 15 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

15 of 15 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check27 Mar

Other references · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  5. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  6. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  7. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  8. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  9. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  10. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  11. 2023Active Retrieval Augmented Generationcited by 506
  12. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
2 more
  1. 2022Re2G: Retrieve, Rerank, Generatecited by 102
  2. 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Modelscited by 63

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applicationscited by 13
  2. 2025Federated Retrieval Augmented Generation for Multi-Product Question Answeringcited by 10
  3. 2025Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filteringcited by 0
  4. 2026Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approachcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  2. 2025MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework
  3. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  4. 2024Knowledge Pyramid Construction for Multi-Level Retrieval-Augmented Generation
  5. 2024DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering
  6. 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity
  7. 2025Multiple Abstraction Level Retrieve Augment Generation
  8. 2026Augmenting Question Answering with A Hybrid RAG Approach