T5 meets Tybalt: Author Attribution in Early Modern English Drama Using Large Language Models
Year: 2023
Type: preprint
Abstract: Large language models have shown breakthrough potential in many NLP domains. Here we consider their use for stylometry, specifically authorship identification in Early Modern English drama. We find both promising and concerning results; LLMs are able to accurately predict the author of surprisingly short passages but are also prone to confidently misattribute texts to specific authors. A fine-tuned t5-large model outperforms all tested baselines, including logistic regression, SVM with a linear kernel, and cosine delta, at attributing small passages. However, we see indications that the presence of certain authors in the model's pre-training data affects predictive results in ways that are difficult to assess.
Source: arXiv (Cornell University)
Authors: Hicke, Rebecca M. M., Mimno, David
Institution: Cornell University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Arcadia Fund
OpenAlex ID: W4388092519