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arXiv · 2502.12328

LM Agents for Coordinating Multi-User Information Gathering

Harsh Jhamtani·Jacob Andreas·Benjamin Van Durme
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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.

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Harsh Jhamtani, Jacob Andreas, Benjamin Van Durme. 2025-02-17. LM Agents for Coordinating Multi-User Information Gathering. https://arxiv.org/abs/2502.12328

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