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This paper conducts research on data intelligent generation and analysis based on the ChatGPT model. To explore ChatGPT's performance and limitations in machine translation tasks, the concepts of the Transformer model and previous studies were reviewed to gain a deep understanding of the principles and roles of components such as attention mechanisms, encoders, decoders, and word embeddings. By controlling ChatGPT through code for machine translation and performing manual verification, the model's limitations in handling synonyms, technical terms, and specific domain languages were identified.
Ruijie Sheng (Fri,) studied this question.
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