@inproceedings{hu-etal-2025-meeting,
title = "{MEETING} {DELEGATE}: Benchmarking {LLM}s on Attending Meetings on Our Behalf",
author = "Hu, Lingxiang and
Yuan, Shurun and
Qin, Xiaoting and
Zhang, Jue and
Lin, Qingwei and
Zhang, Dongmei and
Rajmohan, Saravan and
Zhang, Qi",
editor = "Blodgett, Su Lin and
Curry, Amanda Cercas and
Dev, Sunipa and
Li, Siyan and
Madaio, Michael and
Wang, Jack and
Wu, Sherry Tongshuang and
Xiao, Ziang and
Yang, Diyi",
booktitle = "Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.hcinlp-1.24/",
pages = "283--316",
ISBN = "979-8-89176-353-1",
abstract = "In contemporary workplaces, meetings are essential for exchanging ideas and ensuring team alignment but often face challenges such as time consumption, scheduling conflicts, and inefficient participation. Recent advancements in Large Language Models (LLMs) have demonstrated their strong capabilities in natural language generation and reasoning, prompting the question- can LLMs effectively delegate participants in meetings? To explore this, we develop a prototype LLM-powered meeting delegate system and create a comprehensive benchmark using real meeting transcripts. Our evaluation shows GPT-4/4o balance active and cautious engagement, Gemini 1.5 Pro leans cautious, and Gemini 1.5 Flash and Llama3-8B/70B are more active. About 60{\%} of responses capture at least one key point from the ground truth. Challenges remain in reducing irrelevant or repetitive content and handling transcription errors in real-world settings. We further validate the system through practical deployment and collect feedback. Our results highlight both the promise and limitations of LLMs as meeting delegates, providing insights for their real-world application in reducing meeting burden"
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<abstract>In contemporary workplaces, meetings are essential for exchanging ideas and ensuring team alignment but often face challenges such as time consumption, scheduling conflicts, and inefficient participation. Recent advancements in Large Language Models (LLMs) have demonstrated their strong capabilities in natural language generation and reasoning, prompting the question- can LLMs effectively delegate participants in meetings? To explore this, we develop a prototype LLM-powered meeting delegate system and create a comprehensive benchmark using real meeting transcripts. Our evaluation shows GPT-4/4o balance active and cautious engagement, Gemini 1.5 Pro leans cautious, and Gemini 1.5 Flash and Llama3-8B/70B are more active. About 60% of responses capture at least one key point from the ground truth. Challenges remain in reducing irrelevant or repetitive content and handling transcription errors in real-world settings. We further validate the system through practical deployment and collect feedback. Our results highlight both the promise and limitations of LLMs as meeting delegates, providing insights for their real-world application in reducing meeting burden</abstract>
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%0 Conference Proceedings
%T MEETING DELEGATE: Benchmarking LLMs on Attending Meetings on Our Behalf
%A Hu, Lingxiang
%A Yuan, Shurun
%A Qin, Xiaoting
%A Zhang, Jue
%A Lin, Qingwei
%A Zhang, Dongmei
%A Rajmohan, Saravan
%A Zhang, Qi
%Y Blodgett, Su Lin
%Y Curry, Amanda Cercas
%Y Dev, Sunipa
%Y Li, Siyan
%Y Madaio, Michael
%Y Wang, Jack
%Y Wu, Sherry Tongshuang
%Y Xiao, Ziang
%Y Yang, Diyi
%S Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-353-1
%F hu-etal-2025-meeting
%X In contemporary workplaces, meetings are essential for exchanging ideas and ensuring team alignment but often face challenges such as time consumption, scheduling conflicts, and inefficient participation. Recent advancements in Large Language Models (LLMs) have demonstrated their strong capabilities in natural language generation and reasoning, prompting the question- can LLMs effectively delegate participants in meetings? To explore this, we develop a prototype LLM-powered meeting delegate system and create a comprehensive benchmark using real meeting transcripts. Our evaluation shows GPT-4/4o balance active and cautious engagement, Gemini 1.5 Pro leans cautious, and Gemini 1.5 Flash and Llama3-8B/70B are more active. About 60% of responses capture at least one key point from the ground truth. Challenges remain in reducing irrelevant or repetitive content and handling transcription errors in real-world settings. We further validate the system through practical deployment and collect feedback. Our results highlight both the promise and limitations of LLMs as meeting delegates, providing insights for their real-world application in reducing meeting burden
%U https://aclanthology.org/2025.hcinlp-1.24/
%P 283-316
Markdown (Informal)
[MEETING DELEGATE: Benchmarking LLMs on Attending Meetings on Our Behalf](https://aclanthology.org/2025.hcinlp-1.24/) (Hu et al., HCINLP 2025)
ACL
- Lingxiang Hu, Shurun Yuan, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, and Qi Zhang. 2025. MEETING DELEGATE: Benchmarking LLMs on Attending Meetings on Our Behalf. In Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP), pages 283–316, Suzhou, China. Association for Computational Linguistics.