MeetMulti-X: A benchmark analysis of scaling and prompting large language models on automatic minuting
Year: 2025
Type: article
Abstract: The task of automatic minuting, i.e., capturing all the points from transcripts of multi-party meetings, presents considerable challenges owing to the spontaneous and complex nature of discussions. As organisations increasingly depend on meetings for decision-making, the need for efficient and optimised minuting has intensified, underscoring the shortcomings of manual note-taking due to cognitive overload and diverted participant engagement. This study systematically analyses the impact of scaling Large Language Models (LLMs) on automatic minuting , emphasising key factors including pretrained dataset size, model size, context length, and prompt length. The benchmark evaluation includes both quantitative and qualitative with 19 open-source models (from 77M to 70B parameters) and 4 closed-source models (over 1T parameters) across 4 meeting corpora and prompts. Our findings indicate that (1) models with less than 8B parameters offer a favorable trade-off between performance and efficiency, achieving comparable results to their larger counterparts. (2) scaling pretrained data size improves performance up to a threshold, beyond which gains diminish. (3) context length exhibits a non-linear effect, with optimal performance around 8K-16K tokens. (4) longer prompts consistently degrade output quality, highlighting the need for concise and well-structured prompting. To the best of our knowledge this is the first work, exploring scaling LLMS on automatic minuting. Code is available at https://anonymous.4open.science/r/MeetMultiX-9A36
Source: Expert Systems with Applications
Institution: University of Ulster
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
Awards: G1332530600, Kelvin-2
OpenAlex ID: W4416808698