  This study explores linguistic differences between human and LLM-generated
dialogues, using 19.5K dialogues generated by ChatGPT-3.5 as a companion to the
EmpathicDialogues dataset. The research employs Linguistic Inquiry and Word
Count (LIWC) analysis, comparing ChatGPT-generated conversations with human
conversations across 118 linguistic categories. Results show greater
variability and authenticity in human dialogues, but ChatGPT excels in
categories such as social processes, analytical style, cognition, attentional
focus, and positive emotional tone, reinforcing recent findings of LLMs being
"more human than human." However, no significant difference was found in
positive or negative affect between ChatGPT and human dialogues. Classifier
analysis of dialogue embeddings indicates implicit coding of the valence of
affect despite no explicit mention of affect in the conversations. The research
also contributes a novel, companion ChatGPT-generated dataset of conversations
between two independent chatbots, which were designed to replicate a corpus of
human conversations available for open access and used widely in AI research on
language modeling. Our findings enhance understanding of ChatGPT's linguistic
capabilities and inform ongoing efforts to distinguish between human and
LLM-generated text, which is critical in detecting AI-generated fakes,
misinformation, and disinformation.
