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    <abstract>This paper introduces PeopleJoin, a benchmark for evaluating LM-mediated collaborative problem solving. Given a user request, PeopleJoin agents must identify teammates who might be able to assist, converse with these teammates to gather information, and finally compile a useful answer or summary for the original user. PeopleJoin comprises two evaluation domains: PeopleJoin-QA, focused on questions about tabular data, and PeopleJoin-DocCreation, focused on document creation tasks. The two domains are adapted from existing NLP benchmarks for database question answering and multi-document summarization; here, however, the information needed to complete these tasks is distributed across synthetic “organizations” of 2–20 users, simulating natural multi-user collaboration scenarios. We implemented several popular LM agent architectures, evaluating their accuracy and efficiency at completing tasks, and highlight new research questions that can be studied using PeopleJoin.</abstract>
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%0 Conference Proceedings
%T LLM Agents for Coordinating Multi-User Information Gathering
%A Jhamtani, Harsh
%A Andreas, Jacob
%A Van Durme, Benjamin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F jhamtani-etal-2025-llm
%X This paper introduces PeopleJoin, a benchmark for evaluating LM-mediated collaborative problem solving. Given a user request, PeopleJoin agents must identify teammates who might be able to assist, converse with these teammates to gather information, and finally compile a useful answer or summary for the original user. PeopleJoin comprises two evaluation domains: PeopleJoin-QA, focused on questions about tabular data, and PeopleJoin-DocCreation, focused on document creation tasks. The two domains are adapted from existing NLP benchmarks for database question answering and multi-document summarization; here, however, the information needed to complete these tasks is distributed across synthetic “organizations” of 2–20 users, simulating natural multi-user collaboration scenarios. We implemented several popular LM agent architectures, evaluating their accuracy and efficiency at completing tasks, and highlight new research questions that can be studied using PeopleJoin.
%R 10.18653/v1/2025.findings-acl.916
%U https://aclanthology.org/2025.findings-acl.916/
%U https://doi.org/10.18653/v1/2025.findings-acl.916
%P 17800-17826

Markdown (Informal)

[LLM Agents for Coordinating Multi-User Information Gathering](https://aclanthology.org/2025.findings-acl.916/) (Jhamtani et al., Findings 2025)

• LLM Agents for Coordinating Multi-User Information Gathering (Jhamtani et al., Findings 2025)

ACL

• Harsh Jhamtani, Jacob Andreas, and Benjamin Van Durme. 2025. LLM Agents for Coordinating Multi-User Information Gathering. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17800–17826, Vienna, Austria. Association for Computational Linguistics.