Volume 3,Issue 8
跨越人机界限：人脑与大语言模型在翻译认知机制中的异同比较研究
本研究通过系统比较人脑神经网络和大语言模型（如BERT、GPT 等）在语言翻译中的结构、机制和情感加工差异，深入探讨二者在语言理解、上下文处理与翻译生成方面的优劣势。结果表明，人脑在情感表达和上下文灵活性上具有独特优势，而大语言模型在翻译效率和准确性上表现突出。基于这些发现，我们提出了优化路径：一方面，从人脑认知机制汲取灵感以改进机器翻译模型的情感和文化处理能力；另一方面，利用大语言模型的技术优势提升人类译者的实践能力。特别地，我们探讨了如何在翻译过程中实现情感表达与文化嵌入的平衡，包括设计情感文化标签、多候选译文筛选、人机协同翻译流程等策略。最后，我们结合大语言模型的最新研究进展，为翻译人才培养提供新视角与建议，以期促进人机融合的翻译新范式发展。
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