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

• URL: http://arxiv.org/abs/2308.03983v1
• Date: Tue, 8 Aug 2023 02:00:43 GMT
• Title: SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool
• Authors: Youyang Ng, Daisuke Miyashita, Yasuto Hoshi, Yasuhiro Morioka, Osamu Torii, Tomoya Kodama, Jun Deguchi
• Abstract summary: 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. Retrieval-Centric Generation (RCG) approach separates roles of LLMs and retrievers in context interpretation and knowledge memorization. SimplyRetrieve is an open-source tool with the goal of providing a localized, lightweight, and user-friendly interface to these sophisticated advancements.
• Score: 0.14777718769290524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report [3.4632900249241874]
  This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture combines generative capabilities of Large Language Models (LLMs) with the precision of information retrieval. The practical implications of this research lie in enhancing the reliability of generative AI systems in various sectors.
  arXiv  Detail & Related papers  (2024-10-21T12:21:49Z)
• Large Language Models for Base Station Siting: Intelligent Deployment based on Prompt or Agent [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• Enhancing Knowledge Retrieval with In-Context Learning and Semantic Search through Generative AI [3.9773527114058855]
  We propose a novel methodology that combines the generative capabilities of Large Language Models with the fast and accurate retrieval capabilities of vector databases. The developed model, Generative Text Retrieval (GTR), is adaptable to both unstructured and structured data with minor refinement. The refined model, Generative Tabular Text Retrieval (GTR-T), demonstrated its efficiency in large database querying.
  arXiv  Detail & Related papers  (2024-06-13T23:08:06Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• Self-Retrieval: End-to-End Information Retrieval with One Large Language Model [97.71181484082663]
  We introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture. Self-Retrieval internalizes the retrieval corpus through self-supervised learning, transforms the retrieval process into sequential passage generation, and performs relevance assessment for reranking.
  arXiv  Detail & Related papers  (2024-02-23T18:45:35Z)
• How to Build an AI Tutor that Can Adapt to Any Course and Provide Accurate Answers Using Large Language Model and Retrieval-Augmented Generation [0.0]
  The OpenAI Assistants API allows AI Tutor to easily embed, store, retrieve, and manage files and chat history. The AI Tutor prototype demonstrates its ability to generate relevant, accurate answers with source citations.
  arXiv  Detail & Related papers  (2023-11-29T15:02:46Z)
• GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration [97.68234051078997]
  We discuss how Pyserini can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts. We include a Jupyter Notebook-based walk through the core interoperability features, available on GitHub. We present GAIA Search - a search engine built following previously laid out principles, giving access to four popular large-scale text collections.
  arXiv  Detail & Related papers  (2023-06-02T12:09:59Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)
• Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications [0.5156484100374059]
  We propose a novel approach to support domain-specific Model-Driven Software Engineering (MDSE) for the real-world use-case scenarios of smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT) We argue that the majority of available data in the nature for Artificial Intelligence (AI) are unlabeled. Hence, unsupervised and/or semi-supervised ML approaches are the practical choices. Our proposed approach is fully implemented and integrated with an existing state-of-the-art MDSE tool to serve the CPS/IoT domain.
  arXiv  Detail & Related papers  (2021-07-06T15:51:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.