This 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) reliably predict overall presentation quality. Using a novel corpus of over one hundred 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. Our results demonstrate that LLM-based evaluations align closely with human ratings, proving their validity, reliability, and efficiency. Critically, even very short excerpts (less than 10 percent of a talk) 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. This 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.

本研究探讨了在科学演讲中通过“细分”方法进行有效判断的问题，采用大型语言模型（LLMs）对简短片段与整体演讲质量进行评估。研究发现，甚至很短的片段（少于演讲的10%）也能强烈预测整体评价，这表明演讲的开头部分传达了重要的信息，能够影响持久的印象。这为公共演讲领域的细分研究提供了新的视角，并连接了印象形成理论与大型语言模型及当前人工智能交流研究。

观众参与的艺术：基于大型语言模型的科学演讲细分