[1] Alves, D. M, Pombal, J., Guerreiro. N. M. et al. Tower: An open multilingual large language model for translation-related tasks [DB/OL]. arXiv. org, 2024. https://arxiv.org/abs/2312.05187. (2024-02-27) [2024-12-19]. [2] Anthropic. Claude 3.5 Sonnet [EB/OL]. https://www.anthropic.com/news/claude-3-5-sonnet. (2024-06-21) [2024-12-19]. [3] Barrault, L., Chung, Y. A., Meglioli, M. C. et al. Seamless: Multilingual expressive and streaming speech translation [DB/OL]. arXiv. org, 2023. https://arxiv.org/abs/2312.05187. (2023-12-08) [2024-12-19]. [4] Costa-jussà, M. R., Cross, J. & ?elebi, O. et al. No language left behind: Scaling human-centered machine translation[DB/OL]. arXiv. org, 2022. https://arxiv.org/abs/2207.04672. (2022-07-11) [2024-12-19]. [5] Li, F. & Tian, L. Translation practice and competence enhancement in the age of AI: Applying ChatGPT to translation education [A]. In Kubincová, Z. et al. (eds.). International Symposium on Emerging Technologies for Education [C]. Sydney, Australia: Springer, 2023: 219-230. [6] Ŀukasik, M. W. The future of the translation profession in the era of artificial intelligence. Survey results from Polish translators, translation trainers, and students of translation [J]. Lublin Studies in Modern Languages and Literature, 2024(3): 25-39. [7] OpenAI. GPT-4o mini: Advancing cost-efficient intelligence [EB/OL]. https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/. (2024-07-18) [2024-12-19]. [8] Pym, A. & Hao, Y. How to Augment Language Skills: Generative AI and Machine Translation in Language Learning and Translator Training [M]. London & New York: Routledge, 2025. [9] Siu, S. C. ChatGPT and GPT-4 for professional translators: Exploring the potential of large language models in translation [DB/OL]. SSRN, 2023. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4448091. (2023-05-19) [2024-12-19]. [10] Sizov, F., España-Bonet, C., Van Genabith, J. et al. Analysing translation artifacts: A comparative study of LLMs, NMTs, and human translations [A]. In Haddow B. et al. (eds.). Proceedings of the Ninth Conference on Machine Translation [C]. Miami, Florida, USA: Association for Computational Linguistics, 2024: 1183-1199. [11] Son, J. & Kim, B. Translation performance from the user’s perspective of large language models and neural machine translation systems [J]. Information, 2023,14(10): 1-18. [12] Wang, J., Meng, F., Liang, Y. et al. DRT-o1: Optimized deep reasoning translation via long chain-of-thought [DB/OL]. arXiv. org, 2024. https://arxiv.org/abs/2412.17498. (2024-07-04) [2024-12-19]. [13] Wei, J., Wang, X., Schuurmans, D. et al. Chain-of-thought prompting elicits reasoning in large language models [DB/OL]. arXiv. org, 2022. https://arxiv.org/abs/2201.11903. (2022-01-28) [2024-12-20]. [14] Xu, H., Kim, Y. J., Sharaf, A. et al. A paradigm shift in machine translation: Boosting translation performance of large language models [DB/OL]. arXiv. org, 2024. https://arxiv.org/abs/2309.11674. (2024-02-06) [2024-12-19]. [15] Yan, J., Yan, P., Chen, Y. et al. GPT-4 vs. human translators: A comprehensive evaluation of translation quality across languages, domains, and expertise levels [DB/OL]. arXiv. org, 2024. https://arxiv.org/abs/2407.03658. (2024-07-04) [2024-12-19]. [16] Zhang, B., Haddow, B. & Birch, A. Prompting large language model for machine translation: A case study. [A]. In Krause, A. et al. (eds.). Proceedings of the 40th International Conference on Machine Learning [C]. Honolulu, Hawaii, USA: PMLR, 2023: 41092-41110. [17] 韩子满. 国家军事翻译能力探析 [J]. 上海翻译, 2023(6): 17-22, 95. [18] 胡开宝. 国家外宣翻译能力:构成、现状与未来 [J]. 上海翻译, 2023(4): 1-7, 95. [19] 胡开宝, 高莉. 大语言模型背景下的外语学科发展: 问题与前景 [J]. 外语界, 2024(2): 7-12. [20] 李亚玲, 蔡京京, 柏洁明. 生成式大模型引发的隐私风险及治理路径 [J].智能科学与技术学报, 2024(3): 394-401. [21] 刘世界. 涉海翻译中的机器翻译应用效能:基于BLEU、chrF++和BERTScore指标的综合评估 [J]. 中国海洋大学学报(社会科学版), 2024(2): 21-31. [22] 任文 等. 国家翻译能力研究 [M]. 北京: 商务印书馆, 2023. [23] 任文, 李娟娟. 国家翻译能力研究:概念、要素、意义 [J]. 中国翻译, 2021(4): 5-14, 191. [24] 王华树. 国家翻译技术能力研究: 概念内涵、要素分析和主要特征 [J]. 中国翻译, 2023(2): 35-43, 189. [25] 王华树, 李丹, 梁鑫茹. 文化陷阱与突围之路: 大语言模型时代翻译教学中的文化霸权抵抗策略研究 [J]. 外语教育研究, 2024(4):2-10. [26] 王华树, 梁鑫茹. 人工智能时代翻译技术标准研究 [J]. 中华译学, 2024(2): 197-209. [27] 王华树, 刘世界. 大语言模型对译者主体性的冲击及化解策略研究 [J]. 外语与翻译, 2024(4): 12-18. [28] 王华树, 谢斐. 大语言模型技术驱动下翻译教育实践模式创新研究 [J]. 中国翻译, 2024(2): 70-78. [29] 王华树, 谢亚. ChatGPT时代翻译技术发展及其启示 [J]. 外国语言与文化, 2023(4): 80-89. [30] 文旭, 田亚灵. ChatGPT应用于中国特色话语翻译的有效性研究 [J]. 上海翻译, 2024(2): 27-34, 94-95. [31] 武俊宏, 赵阳, 宗成庆. ChatGPT能力分析与未来展望 [J]. 中国科学基金, 2023(5): 735-742. |