ESTEducational Science Theory2995-48352995-4843Art and Technology10.61369/EST.2025030002Article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https://artdesignp.com/journal/EST/3/3/10.61369/EST.2025030002韩志力,蒋慧仪2025332025-03-20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。人脑翻译机制,大语言模型,情感表达,文化嵌入,神经机器翻译

[1]Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014.[2]Luong M T, Pham H, Manning C D. Effective approaches to attention-based neural machine translation[J]. arXiv preprint arXiv:1508.04025, 2015.[3]Zachlod D, Bludau S, Cichon S, 等. Combined analysis of cytoarchitectonic, molecular and transcriptomic patterns reveal differences in brain organization across human functional brain systems[J]. NeuroImage, 2022, 257: 119286.[4]Kujala J, Matveinen S, van Bijnen S, 等. The relationship between structural properties of frontal cortical regions and response inhibition in 6–14-year-old children[J].Brain and Cognition, 2024, 181: 106220.[5]Friederici A D. The Brain Basis of Language Processing: From Structure to Function[J/OL]. Physiological Reviews, 2011, 91(4): 1357-1392. DOI:10/crdcmj.[6]Brazier C, Rouas J L. Usefulness of Emotional Prosody in Neural Machine Translation[J]. arXiv preprint arXiv:2404.17968, 2024.[7]Brazier C, Rouas J L. Conditioning LLMs with Emotion in Neural Machine Translation[J]. arXiv preprint arXiv:2408.03150, 2024.[8]Kwok L, Bravansky M, Griffin L D. Evaluating cultural adaptability of a large language model via simulation of synthetic personas[J]. arXiv preprint arXiv:2408.06929, 2024.[9]Lindquist K A. The role of language in emotion: existing evidence and future directions[J]. Current opinion in psychology, 2017, 17: 135-139.[10]Liu C C, Koto F, Baldwin T, 等. Are multilingual llms culturally-diverse reasoners? an investigation into multicultural proverbs and sayings[J]. arXiv preprint arXiv:2309.08591, 2023.[11]Troiano E, Klinger R, Padó S. Lost in back-translation: Emotion preservation in neural machine translation[C]//Proceedings of the 28th international conference on computational linguistics. 2020: 4340-4354.[12]Mesquita B. Between us: How cultures create emotions[M]. WW Norton & Company, 2022.[13]Dudy S, Ahmad I S, Kitajima R, 等. Analyzing Cultural Representations of Emotions in LLMs Through Mixed Emotion Survey[C/OL]//2024 12th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE Computer Society, 2024: 346-354.https://www.computer.org/csdl/proceedings-article/acii/2024/164300a346/26aU0uVjTgc. DOI:10.1109/ACII63134.2024.00044.[14]Belay T D, Ahmed A H, Grissom II A, 等. CULEMO: Cultural Lenses on Emotion–Benchmarking LLMs for Cross-Cultural Emotion Understanding[J]. arXiv preprint arXiv:2503.10688, 2025.[15]Thakur M. Culturally-Grounded Chain-of-Thought (CG-CoT):Enhancing LLM Performance on Culturally-Specific Tasks in Low-Resource Languages[A/OL]. arXiv,2025. http://arxiv.org/abs/2506.01190. DOI:10.48550/arXiv.2506.01190.