mlsanityData to 14 Oct 2026

SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool

arXiv 2308.03983 ↗First posted 8 Aug 2023cs.CL cs.AIcited by 6

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Abstract (arXiv)

Large Language Model (LLM) based Generative AI systems have seen significant progress in recent years. Integrating a knowledge retrieval architecture allows for seamless integration of private data into publicly available Generative AI systems using pre-trained LLM without requiring additional model fine-tuning. Moreover, Retrieval-Centric Generation (RCG) approach, a promising future research direction that explicitly separates roles of LLMs and retrievers in context interpretation and knowledge memorization, potentially leads to more efficient implementation. SimplyRetrieve is an open-source tool with the goal of providing a localized, lightweight, and user-friendly interface to these sophisticated advancements to the machine learning community. SimplyRetrieve features a GUI and API based RCG platform, assisted by a Private Knowledge Base Constructor and a Retrieval Tuning Module. By leveraging these capabilities, users can explore the potential of RCG for improving generative AI performance while maintaining privacy standards. The tool is available at https://github.com/RCGAI/SimplyRetrieve with an MIT license.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 25 of its 35 references that are in the map. First posted on arXiv 8 Aug 2023.

Builds on

25 of 35 references.

References · 25

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13 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 0; 2026: 1.

6 papers.

  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  2. 2024CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Datacited by 37
  3. 2023Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentationcited by 11
  4. 2026SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systemscited by 7
  5. 2023RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Modelscited by 4
  6. 2024Customized FinGPT Search Agents Using Foundation Modelscited by 1