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Examining Lexical Alignment in Human-Agent Conversations with GPT-3.5 and GPT-4 Models

Wang, Boxuan (2023) Examining Lexical Alignment in Human-Agent Conversations with GPT-3.5 and GPT-4 Models.

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Abstract: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. By providing insights into these aspects, this research aims to contribute to the development of more engaging and effective conversational agents.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Link to this item:https://purl.utwente.nl/essays/95877
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