# Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution

arXiv: https://arxiv.org/abs/1708.05536 · HTML: https://mlsanity.com/paper/1708.05536/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2017.

## Built on by (most cited first, 5 of 5)

- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [Artificial Text Detection via Examining the Topology of Attention Maps](https://mlsanity.com/paper/2109.04825/) (2021)
- [Fingerprinting Fine-tuned Language Models in the Wild](https://mlsanity.com/paper/2106.01703/) (2021)
- [DeepPavlov at SemEval-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated Texts](https://mlsanity.com/paper/2405.10629/) (2024)
- [Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation](https://mlsanity.com/paper/2403.11265/) (2024)

## Abstract (arXiv)

Recent applications of neural language models have led to an increased interest in the automatic generation of natural language. However impressive, the evaluation of neurally generated text has so far remained rather informal and anecdotal. Here, we present an attempt at the systematic assessment of one aspect of the quality of neurally generated text. We focus on a specific aspect of neural language generation: its ability to reproduce authorial writing styles. Using established models for authorship attribution, we empirically assess the stylistic qualities of neurally generated text. In comparison to conventional language models, neural models generate fuzzier text that is relatively harder to attribute correctly. Nevertheless, our results also suggest that neurally generated text offers more valuable perspectives for the augmentation of training data.