The art of audience engagement: LLM-based thin-slicing of scientific talks - Schmälzle, Ralf; Lim, Sue; Du, Yuetong; Bente, Gary (Frontiers in Communication, 2025)
IntroductionThis paper examines the thin-slicing approach—the ability to make accurate judgments based on minimal information—in the context of scientific presentations. Drawing on research from nonverbal communication and personality psychology, we show that brief excerpts (thin slices) of transcribed texts from real presentations reliably predict overall quality evaluations.MethodsUsing a novel corpus of over 100 real-life science talks, we employ Large Language Models (LLMs) to evaluate transcripts of full presentations and their thin slices. By correlating LLM-based evaluations of short excerpts with full-talk assessments, we determine how much information is needed for accurate predictions.ResultsOur results demonstrate that LLM-based evaluations align closely with human evaluations, proving their validity, reliability, and efficiency. Critically, even very short excerpts (<10% of a talk’s transcript) strongly predict overall evaluations. This suggests that the first moments of a presentation convey relevant information that is used in quality evaluations and can shape lasting impressions. The findings are robust across different LLMs and prompting strategies.DiscussionThis work extends thin-slicing research to public speaking and connects theories of impression formation to LLMs and current research on AI communication. We discuss implications for communication and social cognition research on message reception. Lastly, we suggest an LLM-based thin-slicing framework as a scalable feedback tool to enhance human communication.