Large language models (LLMs) are an emerging site for computational literary and cultural analysis. While such research has focused on applying LLMs to the analysis of literary text passages, the probabilistic mechanism used by these models for text generation lends them to also understanding literary and cultural trends. Indeed, we can imagine LLMs as constructing their own “literary canons” by encoding particular authors and book titles with high probability distributions around relevant words and text. This paper explores the frequency with which certain literary titles and authors are generated by a selection of popular proprietary and open-source models and compares it to existing conceptions of literary canon. It investigates the diversity of author mentions across gender, ethnicity, nationality as well as LLMs’ ability to accurately report such characteristics. We demonstrate that the literary canons of popular large-language models are generally aligned with the Western literary canon in that they slightly prioritize male authors and overwhelmingly prioritize White American and British authors.

Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of humanities and social science literature analyzing such biases in children stories. Because these analyses are often conducted manually and at a small scale, such investigations can benefit from the use of more recent natural language processing (NLP) methods that examine social bias in models and data corpora. Our work joins this interdisciplinary effort and makes a unique contribution by taking into account the event narrative structures when analyzing the social bias of stories. We propose a computational pipeline that automatically extracts a story’s temporal narrative verb-based event chain for each of its characters as well as character attributes such as gender. We also present a verb-based event annotation scheme that can facilitate bias analysis by including categories such as those that align with traditional stereotypes. Through a case study analyzing gender bias in fairy tales, we demonstrate that our framework can reveal bias in not only the unigram verb-based events in which female and male characters participate but also in the temporal narrative order of such event participation.