Hanxiao Ma
School of Foreign Languages, Gannan Normal University, Ganzhou 341000, Jiangxi, China.
*Corresponding author: Hanxiao Ma
Abstract
This paper aims to compare and analyze the performance of the Qwen large language model (LLM) and human translation in literary translation, using the Chinese translations of The Old Man and the Sea as a case study. The research involved inputting two different instructions into Qwen for translation and comparing the results with Sun Zhili's 2012 Chinese translation. The three resulting translations were analyzed using the Wordless (for lexical density, richness, and diversity) and Alpha Readability Chinese (for syntactic complexity) software, focusing on multiple dimensions such as lexical density, richness, and diversity, as well as syntactic complexity. The findings indicate that Qwen excels in lexical density, accurately conveying the semantic information of the original text. However, it still falls short compared to human translation in terms of lexical richness and diversity. This study shows that although large language models have made significant progress in translation, human translation retains an irreplaceable advantage in the field of literary translation, which heavily depends on context and expressive artistry. Therefore, future efforts should explore the integration of artificial intelligence and human translation to enhance the overall quality of literary translation.
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How to cite this paper
Corpus-based Comparative Analysis of Large Language Model and Human Translation: A Case Study of the Chinese Version of The Old Man and the Sea
How to cite this paper: Hanxiao Ma. (2025). Corpus-based Comparative Analysis of Large Language Model and Human Translation: A Case Study of the Chinese Version of The Old Man and the Sea. Translation and Foreign Language Learning, 1(1), 61-65.
DOI: http://dx.doi.org/10.26855/tfll.2025.08.010