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Information Retrieval Meets Large Language Models

Published: 13 May 2024 Publication History

Abstract

The advent of large language models (LLMs) presents both opportunities and challenges for the information retrieval (IR) community. On one hand, LLMs will revolutionize how people access information, meanwhile the retrieval techniques can play a crucial role in addressing many inherent limitations of LLMs. On the other hand, there are open problems regarding the collaboration of retrieval and generation, the potential risks of misinformation, and the concerns about cost-effectiveness. To seize the critical moment for development, it calls for the joint effort from academia and industry on many key issues, including identification of new research problems, proposal of new techniques, and creation of new evaluation protocols. It has been one year since the launch of ChatGPT in November last year, and the entire community is currently undergoing a profound transformation in techniques. Therefore, this workshop will be a timely venue to exchange ideas and forge collaborations. The organizers, committee members, and invited speakers are composed of a diverse group of researchers coming from leading institutions in the world. This event will be made up of multiple sessions, including invited talks, paper presentations, hands-on tutorials, and panel discussions. All the materials collected for this workshop will be archived and shared publicly, which will present a long-term value to the community.

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  1. Information Retrieval Meets Large Language Models

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    cover image ACM Conferences
    WWW '24: Companion Proceedings of the ACM Web Conference 2024
    May 2024
    1928 pages
    ISBN:9798400701726
    DOI:10.1145/3589335
    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

    Publication History

    Published: 13 May 2024

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    Author Tags

    1. information retrieval
    2. large language models
    3. question answering
    4. ranking
    5. retrieval-augmented generation
    6. search

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    Funding Sources

    • National Key R&D Program of China

    Conference

    WWW '24
    Sponsor:
    WWW '24: The ACM Web Conference 2024
    May 13 - 17, 2024
    Singapore, Singapore

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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