# A Stylometric Application of Large Language Models

arXiv: https://arxiv.org/abs/2510.21958 · HTML: https://mlsanity.com/paper/2510.21958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [The Limitations of Stylometry for Detecting Machine-Generated Fake News](https://mlsanity.com/paper/1908.09805/) (2019)
- [Misleading Authorship Attribution of Source Code using Adversarial Learning](https://mlsanity.com/paper/1905.12386/) (2019)
- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [Separating Style from Substance: Enhancing Cross-Genre Authorship Attribution through Data Selection and Presentation](https://mlsanity.com/paper/2408.05192/) (2024)
- [Mapping Mutable Genres in Structurally Complex Volumes](https://mlsanity.com/paper/1309.3323/) (2013)

## Abstract (arXiv)

We show that large language models (LLMs) can be used to distinguish the writings of different authors. Specifically, an individual GPT-2 model, trained from scratch on the works of one author, will predict held-out text from that author more accurately than held-out text from other authors. We suggest that, in this way, a model trained on one author's works embodies the unique writing style of that author. We first demonstrate our approach on books written by eight different (known) authors. We also use this approach to confirm R. P. Thompson's authorship of the well-studied 15th book of the Oz series, originally attributed to F. L. Baum.