Large language models like GPT have proven widely successful on natural
language understanding tasks based on written text documents. In this paper, we
investigate an LLM's performance on recordings of a group oral communication
task in which utterances are often truncated or not well-formed. We propose a
new group task experiment involving a puzzle with several milestones that can
be achieved in any order. We investigate methods for processing transcripts to
detect if, when, and by whom a milestone has been completed. We demonstrate
that iteratively prompting GPT with transcription chunks outperforms semantic
similarity search methods using text embeddings, and further discuss the
quality and randomness of GPT responses under different context window sizes.

调查了 LLM 在小组口头交流任务中的表现，提出了使用文字片段迭代提示 GPT 来处理转录内容以检测是否完成了里程碑。研究发现，相比于使用文本嵌入的语义相似度搜索方法，迭代提示 GPT 的表现更好，并进一步讨论了在不同上下文窗口大小下 GPT 响应的质量和随机性。