Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914169645694976 |
|---|---|
| author | Yao, Xiaofang Kang, Yong-Bin McCosker, Anthony |
| author_facet | Yao, Xiaofang Kang, Yong-Bin McCosker, Anthony |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature Yao, Xiaofang Kang, Yong-Bin McCosker, Anthony Computation and Language Artificial Intelligence J.5; I.7.1 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. |
| title | Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature |
| topic | Computation and Language Artificial Intelligence J.5; I.7.1 |
| url | https://arxiv.org/abs/2506.13013 |