# Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature

arXiv: https://arxiv.org/abs/2506.13013 · HTML: https://mlsanity.com/paper/2506.13013/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 71 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Towards Making the Most of ChatGPT for Machine Translation](https://mlsanity.com/paper/2303.13780/) (2023)
- [Stylometric Detection of AI-Generated Text in Twitter Timelines](https://mlsanity.com/paper/2303.03697/) (2023)
- [BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation](https://mlsanity.com/paper/2103.11878/) (2021)
- [Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature](https://mlsanity.com/paper/2210.14250/) (2022)
- [How to Design Translation Prompts for ChatGPT: An Empirical Study](https://mlsanity.com/paper/2304.02182/) (2023)
- [Do GPTs Produce Less Literal Translations?](https://mlsanity.com/paper/2305.16806/) (2023)
- [Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis](https://mlsanity.com/paper/2304.05534/) (2023)
- [Creativity in translation: machine translation as a constraint for literary texts](https://mlsanity.com/paper/2204.05655/) (2022)
- [Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus](https://mlsanity.com/paper/2305.11142/) (2023)

## Abstract (arXiv)

Existing research indicates that machine translations (MTs) of literary texts are often unsatisfactory. MTs are typically evaluated using automated metrics and subjective human ratings, with limited focus on stylistic features. Evidence is also limited on whether state-of-the-art large language models (LLMs) will reshape literary translation. This study examines the stylistic features of LLM translations, comparing GPT-4's performance to human translations in a Chinese online literature task. Computational stylometry analysis shows that GPT-4 translations closely align with human translations in lexical, syntactic, and content features, suggesting that LLMs might replicate the 'human touch' in literary translation style. These findings offer insights into AI's impact on literary translation from a posthuman perspective, where distinctions between machine and human translations become increasingly blurry.