Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature
This addresses the issue of assessing AI's role in literary translation for researchers and translators, showing incremental progress in understanding stylistic replication.
The study tackled the problem of evaluating stylistic features in machine translations of literary texts, finding that GPT-4 translations closely align with human translations in lexical, syntactic, and content features, suggesting LLMs can replicate the 'human touch' in style.
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.