Modern Social Science Research

To achieve a balance between translation quality and efficiency in agricultural science and technology translation using large language models, post-editing has emerged as a key translation implementation method. This study employs the Illustrated Handbook of Maize Diseases, Insect Pests and Weeds in China as its corpus and based on the Multidimensional Quality Metrics (MQM) framework, systematically evaluates the translation quality of ERNIE Bot and DeepSeek in Chinese-to-English agricultural science and technology translation tasks. The study reveals both common deficiencies and distinct weaknesses between the two models. Accuracy errors account for the highest proportion (47.56% for ERNIE Bot and 41.92% for DeepSeek), primarily manifesting as distortions in key information such as morphological parameters and control data. In terms of differences, ERNIE Bot exhibits more prominent accuracy errors, while DeepSeek shows more pronounced deficiencies in formatting and stylistic norms. To address these issues, this paper proposes three post-editing strategies: correcting mistranslations and supplementing omissions to enhance accuracy, tracing term origins and conducting knowledge verification to reduce terminology errors, and adding explanatory supplements to strengthen locale-convention adaptation. The study confirms that general-purpose large language models still require human intervention in agricultural science and technology translation, and that post-editing should focus on the precise reconstruction of specialized knowledge and the deep adaptation of cultural contexts.

Large language models; Translation quality assessment; MQM framework; Post-editing; Agricultural science and technology translation