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short-paper

CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models

Authors:

Jiaxin MaoAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2729 - 2733

https://doi.org/10.1145/3626772.3657672

Published: 11 July 2024 Publication History

Abstract

Collaborative search supports multiple users working together to accomplish a specific search task. Research has found that designing lightweight collaborative search plugins within instant messaging platforms aligns better with users' collaborative habits. However, due to the complexity of multi-user interaction scenarios, it is challenging to implement a fully functioning lightweight collaborative search system. Therefore, previous studies on lightweight collaborative search had to rely on the Wizard of Oz paradigm. In recent years, large language models (LLMs) have been demonstrated to interact naturally with users and achieve complex information-seeking tasks through LLM-based agents. Hence, to better support the research in collaborative search, in this demo, we propose CoSearchAgent, a lightweight collaborative search agent powered by LLMs. CoSearchAgent is designed as a Slack plugin that can support collaborative search during multi-party conversations on this platform. Equipped with the capacity to understand the queries and context in multi-user conversations and the ability to search the Web for relevant information via APIs, CoSearchAgent can respond to user queries with answers grounded on the relevant search results. It can also ask clarifying questions when the information needs are unclear. The proposed CoSearchAgent is highly flexible and would be useful for supporting further research on collaborative search. The code and demo are accessible at https://github.com/pygongnlp/CoSearchAgent

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Cited By

Xu HSerra ESpezzano F(2024)Towards Seamless User Query to REST API ConversionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680275(5495-5498)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680275

Index Terms

CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models
1. Information systems
  1. Information retrieval
    1. Search engine architectures and scalability

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Published In

cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

Copyright © 2024 ACM.

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Published: 11 July 2024

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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 14 - 18, 2024

Washington DC, USA

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Cited By

Xu HSerra ESpezzano F(2024)Towards Seamless User Query to REST API ConversionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680275(5495-5498)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680275

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