CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories
Anneliese Brei, Abhisheik Sharma Department of Computer Science, University of North Carolina at Chapel Hill and Georgia Institute of Technology, Nicholas Sanaie, Lu Wang University of Michigan, Snigdha Chaturvedi Department of Computer Science, University of North Carolina at Chapel Hill
As LLM-generated text is increasingly used, especially in fictional domains, we explore how much LLM-generated stories differ from human-written stories. In this work, we focus on characters. We borrow definitions from narratology to analyze 8 intricate category-pairs of character, such as stylization and wholeness. These category-pairs consider more than just basic characteristics. They assess how characters are portrayed within their stories. After automatically inferring categories of characters within both LLM and human-written stories, we compare and contrast these two sets of stories. We consider the following overarching questions: (1) Do LLMs and human-written stories have similar characters? and (2) Do LLMs generate stories with a variety of characters? Our analysis includes research questions that focus on stories generated by popular LLMs and recently published human-written stories. We describe a number of interesting similarities, differences and key takeaways.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表