Examining Lexical Alignment in Human-Agent Conversations with GPT-3.5 and GPT-4 Models
摘要
This study employs a quantitative approach to investigate lexical alignment in human-agent interactions involving GPT-3.5 and GPT-4 language models. The research examines alignment performances across different conversational contexts and compares the performance of the two models. The findings highlight the significant improvements in GPT-4’s ability to foster lexical alignment, and the influence of conversation topics on alignment patterns.