Capturing Authorship Style Through Large Language Models

Abstract

This work explores the use of large language model (LLM) embeddings to capture style in authorship attribution tasks. By applying a Siamese network to embeddings from OpenAI’s text-embedding-ada-002, we assess whether style embeddings can distinguish authors across unseen texts. The results show a strong attribution accuracy that outperforms traditional characteristics, though the performance declines with more authors and improves with longer texts.