Research on AI Translation Quality of Agricultural Science and Technology Texts Based on Prompt Optimization
Abstract
With the increasing application of artificial intelligence in translation practice, AI-assisted translation has become an important tool
in the translation of agricultural science and technology texts. However, due to the complexity and rigor of scientific discourse, AI-generated
translations often show deficiencies in logical clarity and academic expression, especially in Chinese-English translation. This paper takes the
DeepSeek model as an example to investigate how prompt optimization can improve the quality of AI translation of agricultural science and
technology texts. Based on experimental data from Chinese-English translation samples, this study analyzes the optimized and unoptimized
outputs from the perspectives of logical structure, semantic accuracy, and academic writing conventions. The results show that prompt optimization significantly enhances the clarity and precision of AI translations, upgrading them from generally accurate renditions to academically
refined texts. The findings provide practical implications for MTI students and translators in agricultural and forestry universities.
in the translation of agricultural science and technology texts. However, due to the complexity and rigor of scientific discourse, AI-generated
translations often show deficiencies in logical clarity and academic expression, especially in Chinese-English translation. This paper takes the
DeepSeek model as an example to investigate how prompt optimization can improve the quality of AI translation of agricultural science and
technology texts. Based on experimental data from Chinese-English translation samples, this study analyzes the optimized and unoptimized
outputs from the perspectives of logical structure, semantic accuracy, and academic writing conventions. The results show that prompt optimization significantly enhances the clarity and precision of AI translations, upgrading them from generally accurate renditions to academically
refined texts. The findings provide practical implications for MTI students and translators in agricultural and forestry universities.
Keywords
AI translation; Prompt optimization; Chinese-English translation
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PDFReferences
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DOI: http://dx.doi.org/10.70711/rcha.v4i1.8879
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