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

TL;DR

This study compares GPT-4's Chinese literary translations to human translations using stylometry, revealing GPT-4's ability to mimic human stylistic features and challenging the distinction between machine and human translation quality.

Contribution

It provides a computational analysis showing GPT-4's translations closely resemble human stylistic features, highlighting AI's potential in literary translation.

Findings

GPT-4's translations align with human stylistic features

Stylometry analysis shows close lexical, syntactic, and content similarity

Implications for AI's role in blurring human-machine translation boundaries

Abstract

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.