Stylistic variation across English translations of Chinese science fiction: Ken Liu versus ChatGPT - Zhou, Pingdi; Cheng, Jiajun (Frontiers in Artificial Intelligence, 2025)
Advancements in computational tools, including neural machine translation (NMT) and large language models (LLMs), have revolutionized literary stylistics and opened new avenues in corpus-based translation studies (CBTS). Yet, the style of LLM-produced translations, especially in science fiction (SF) literature, remain understudied. This study examines stylistic variation across English translations of Chinese SF by translator Ken Liu and ChatGPT-4o. Thirteen works translated by both were compared using Multi-Dimensional analysis on key dimensions. Stylometric tests assessed within-translator and between-translator variations, and functional analysis interpreted the subordinate linguistic features. Findings reveal that Ken Liu adapts his style to each story’s depth, exhibiting greater variation, while GPT maintains a more consistent style. Ken Liu’s less narrative style enhances resonance through a minimalist approach, whereas GPT’s more narrative style offers clarity but may undermine thematic impact. The study contributes to CBTS by providing a methodological framework for comparing human and LLM translations in terms of style. It highlights a collaborative model that combines human creativity with LLM efficiency, necessitating continuous upskilling among students, educators, and practitioners to adapt to LLMs’ growing presence in translation. Ultimately, by exploring the intersection of linguistics, literature, and artificial intelligence, the study pushes the boundaries of translation studies and practices.