arXiv Machine Learning By Mohammed Azeez Khan, Aaron Dsouza, Ashutosh Mishra, Ummul Faiz Z. Bibi, Neha K. Nair, Amar Kumar Behera

Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison

Read the original on arXiv Machine Learning →

The study compares human Project Manager facilitation with Llama 3.1 8B Instruct responses in scenario‑based collaborative design meetings, analyzing 199 paired contexts from the AMI Meeting Corpus. Five behavioral dimensions—contextual semantic distance, lexical elaboration, empathy‑and‑hedge lexical density, topic divergence, and phase‑vocabulary alignment—were examined. Human facilitation showed higher contextual semantic distance and empathy‑and‑hedge density, while Llama facilitation exhibited greater topic divergence, lexical elaboration, and instruction‑conditioned phase alignment.

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arXiv AI
Sep 4

Speak for Me: Giving LLMs the Situational Awareness to Participate in a Meeting

The paper introduces CAPA, a Collaborative Agent Predictive Architecture designed to give large language model (LLM) agents situational awareness in online meetings. CAPA uses a Perceiver to update meeting state, a Predictor to forecast conversation flow, a Controller to decide speaking actions, and a Generator to phrase contributions. Evaluated on 137 AMI meetings, CAPA reduces the silence rate from 51.4% to 2.5%, doubles credited recovery, and maintains low hallucination, demonstrating that structured state tracking is key to effective delegation.

By Muneeb Khan, Frederic Kirstein, Terry Ruas, Bela Gipp