The Literary Canons of Large-Language Models: An Exploration of the Frequency of Novel and Author Generations Across Gender, Race and Ethnicity, and Nationality (2025)

Abstract: 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.

## introduction large language models (llms) are an emerging site for computational literary and cultural analysis. such research typically covers methods and evaluations for applying llms to creative writing @xcite and literary analysis @xcite or for exploring the extent to which these models have been trained on partial or full literary texts @xcite . however, the probabilistic mechanism used by these models for text generation @xcite lends them to use for also understanding literary and cultural trends. indeed, we can imagine llms as constructing their own "literary canon" that form the basis of downstream tasks centered around literature such as recommendation, classification, and question-answering. traditionally, the debate about inclusion of texts in the canon has been held in undergraduate literature departments when determining core curriculum. the debate, rooted in the heterogeneous definitions of "classics" foot_0 and "canon," foot_1 becomes more convoluted in literary criticism in the last century. the adjective "classics" evolved from signifying greco-roman antiquity "of the first class, or the highest rank or importance" to indicating a more general representation of art and literature over the past three centuries @xcite . it was not until 1929 where literary criticism was appended to the entry for "canon" relating the noun directly to a "body of literary works traditionally regarded as the most important, significant, and worthy of study; those works of esp. western literature considered to be established as being of the highest quality and most enduring value; the classics (now frequently in the canon)" (oxford english dictionary, 2024a). richard ohmann's definition furthers that the canon is a "shared understanding of what literature is worth preserving" @xcite . alternatively, @xcite discusses how maintaining the canon as a static form of representation is problematic, given that it inherently includes the elite, while further excluding social groups without power. in our study, we consider if large language models "preserve" specific works of literature by encoding them with high probability distributions around relevant words and text. we suspect that because of repetition bias in model training and data set quality limitations, llms may proliferate the marginalization of specific marginalized demographics and the solidify the elite in literature. as people rely more on llm-powered assistants or search engines to discover literary works, it becomes imperative to understand how these models generate recommendations. this paper explores the frequency with which certain literary titles and authors are generated by a selection of proprietary and open source models and compares them to existing conceptions of the literary canon. as mentioned above, the western canon has long prioritized works written by male, white european authors with relatively recent strong push-back from post-colonial and feminist critiques @xcite @xcite . following these critical perspectives, we analyze the diversity of author mentions across gender, ethnicity, and nationality. additionally, we investigate the extent to which llms can produce accurate demographic information of authors. this paper brings several novel contributions to the field of cultural analysis of large-language models: ## authordemodb we present litauthordemodb, an open-source dataset of classic and contemporary authors with corresponding demographic information including gender, race/ethnicity, and nationality. while author datasets such as gale's books and authors database and isbndb exist, they are not easily or freely accessible. indeed, there is no direct download of datasets or api access to easily match an author to demographic information. litauthordemodb is meant to provide readers and researchers an accessible, open-source, and community-updated and reviewed repository for author demographics. it is available for download at https://github.com/ibm/litauthordemodb . we plan to continually update with new authors and fields, particularly to increase the diversity of the dataset. the first version of the dataset contains a total of 1,345 authors and 2,238 corresponding titles. in appendix c, the next version of the dataset will draw from a variety of genres as well as other sources such as wikidata with a focus on increasing gender, racial, and national diversity. users will also be able to suggest corrections and new authors. ## conclusion our evidence suggests that the literary canons of popular large-language models are generally aligned with common conceptions of the the western literary canon in that they slightly prioritize male authors and overwhelmingly prioritize white american and english authors particularly in comparison to global population demographics. this behavior occurs even when explicitly prompting models for a broader 'global" canon. we advocate for a globalized representation of canonical standards within llms, using our dataset as a vehicle to align output to better reflect international demographics. we are concerned that ancient, historical, and contemporary texts from entire continents such as africa and asia and aboriginal and native cultures from the americas account for less than nine percent of nationalities represented in the "llm canon". additionally, while llms appear to accurately reproduce demographic information, further study should be considered with concerns over personal identity and biographical fact. other potential areas for further study include: prompting models in different languages; running experiments with different sampling parameters; investigating the diversity of popular and genre literature; including other demographic information such as lgbtqia+ status; and evaluating the model's ability to complete more complex tasks such as question-answering of titles written by a diverse set of authors. we urge our readers to contribute to our litauthordemodb as our hope is to leverage it to re-train llms with a more diverse, representative canon, impacting future analysis, scholarship, and readership.