Application and Limitations of Large Language Models in the Translation of the Contract Book of the Civil Code

Abstract

In recent years, the rapid advancement of Large Language Models (LLMs) has created new possibilities for legal translation. This study employs a combination of quantitative analysis and case studies to systematically compare the textual features of the authoritative human-translated version and the GPT-4o-translated version of the Contract Book of the Civil Code across three dimensions: terminological accuracy, syntactic complexity, and the handling of semantically ambiguous expressions. The findings are as follows: (1) In trans-lating the Contract Book of the Civil Code, the human version demonstrates su-perior rigor and precision in legal terminology, in contrast, the GPT-generated version displays deviations in term usage that may introduce ambiguities in le-gal interpretation. (2) Although the GPT version enhances readability by reduc-ing syntactic complexity, this simplification compromises the precision and formality required in legal discourse. (3) In addressing semantically ambiguous expressions, human translators employ terminological transformation, lexical supplementation, and tense modulation, while the GPT version tends to rely on literal translation, increasing the risk of omission or distortion in legal provi-sions. This study highlights the current limitations of LLMs in legal translation and underscores the importance of human-machine collaboration, offering in-sights and guidance for producing high-quality legal translations with the assis-tance of LLMs.