Chinese–English Intelligent Translation Leveraging Large Language Modeling and Correction Mechanisms

In the contemporary era of globalization, the frequency of information exchange across languages has been escalating exponentially. The role of translation as a crucial conduit for cross-linguistic communication is increasingly conspicuous. By virtue of their robust language generation capacity and profound understanding of linguistic patterns, the Large Language Modellings (LLMs) are capable of facilitating swift and accurate translation between Chinese and English. However, due to the limitations of pre-trained corpus quality and language distribution, translations generated by LLMs still have some low-quality translation problems, including mistranslation, omission, hallucination and off-target translation. In order to improve the translation quality of the LLM, the authors propose a translation error-prone word correction mechanism. The experiment employed the Llama2-7B model and validated it across six language directions in the WMT2022 test set, namely. The results showed that compared with the uncorrected translations, the average COMET and the BLEU scores of the X-English translation language direction were improved by 1.87 and 1.26 points, respectively, and the average COMET and BLEU scores of the English-X language direction were improved by 8.79 and 7.67 points, respectively. The experiments had demonstrated that word correction mechanism can enhance the quality of the text translation effectively. Through experimental validation, the method has achieved significant improvement in translation quality, providing strong support for the development of the field of Chinese–English translation, and laying a solid foundation for the future research and application of multilingual translation technology.