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The Journey to A Knowledgeable Assistant with Retrieval-Augmented Generation (RAG)

Published: 04 March 2024 Publication History

Abstract

Large Language Models (LLMs) have demonstrated strong capabilities in comprehending and generating human language, as well as emerging abilities like reasoning and using tools. These advancements have been revolutionizing techniques in every front, including the development of personal assistants. However, their inherent limitations such as lack of factuality and hallucinations make LLMs less suitable for creating knowledgeable and trustworthy assistants.
In this talk, we describe our journey in building a knowledgeable AI assistant by harnessing LLM techniques. We start with a comprehensive set of experiments designed to answer the questions of \em how reliable are LLMs on answering factual questions and \em how the performance differs across different types of factual knowledge. Subsequently, we constructed a \em federated Retrieval-Augmented Generation (RAG) system that integrates external information from both the web and knowledge graphs in text generation. This system supports conversation functionality for the Ray-ban Meta smart glasses, providing trustworthy information on real-time topics like stocks and sports, and information on torso-to-tail entities such as local restaurants. Additionally, we are exploring the potential of external knowledge to facilitate multi-modal Q&A. We will share our techniques, our findings, and the path forward in this talk.

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  1. The Journey to A Knowledgeable Assistant with Retrieval-Augmented Generation (RAG)

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    cover image ACM Conferences
    WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining
    March 2024
    1246 pages
    ISBN:9798400703713
    DOI:10.1145/3616855
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 04 March 2024

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    1. ai assistant
    2. large language model
    3. retrieval-augmented generation (rag)

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