Machine Translation (MT) has made significant progress in recent years using deep learning, especially after the emergence of large language models (LLMs) such as GPT-3 and ChatGPT. This brings new challenges and opportunities for MT using LLMs. In this paper, we brainstorm some interesting directions for MT using LLMs, including stylized MT, interactive MT, and Translation Memory-based MT, as well as a new evaluation paradigm using LLMs. We also discuss the privacy concerns in MT using LLMs and a basic privacy-preserving method to mitigate such risks. To illustrate the potential of our proposed directions, we present several examples for the new directions mentioned above, demonstrating the feasibility of the proposed directions and highlight the opportunities and challenges for future research in MT using LLMs.

翻译：近年来，机器翻译（MT）借助深度学习取得了显著进展，尤其是在GPT-3和ChatGPT等大型语言模型（LLMs）出现之后。这为基于LLMs的机器翻译带来了新的挑战与机遇。本文探讨了基于LLMs的机器翻译的几个有趣方向，包括风格化机器翻译、交互式机器翻译、基于翻译记忆库的机器翻译，以及利用LLMs的新型评估范式。我们还讨论了基于LLMs的机器翻译中的隐私问题，并提出了一种基本的隐私保护方法来降低此类风险。为展示所提方向的潜力，我们针对上述新方向提供了若干实例，验证了这些方向的可行性，并指出了未来基于LLMs的机器翻译研究中的机遇与挑战。