Generative artificial intelligence tools like large language models are rapidly transforming academic research and real world applications. However, discussions on ethical guidelines for generative AI in science remain fragmented, underscoring the urgent need for consensus based standards. This paper offers an initial framework by developing analyses and mitigation strategies across five key themes: understanding model limitations regarding truthfulness and bias; respecting privacy, confidentiality, and copyright; avoiding plagiarism and policy violations when incorporating model output; ensuring applications provide overall benefit; and using AI transparently and reproducibly. Common scenarios are outlined to demonstrate potential ethical violations. We argue that global consensus coupled with professional training and reasonable enforcement are critical to promoting the benefits of AI while safeguarding research integrity.

翻译：生成式人工智能工具（如大型语言模型）正迅速改变学术研究与实际应用。然而，关于科学领域中生成式AI伦理准则的讨论仍显零散，凸显出建立共识性标准的紧迫性。本文提出初步框架，围绕五个关键主题展开分析与缓解策略：理解模型在真实性和偏见方面的局限性；尊重隐私、保密性与版权；在整合模型输出时避免剽窃与政策违规；确保应用带来整体收益；以及透明且可重复地使用AI。文中概述常见场景以展示潜在伦理违规行为。我们认为，全球共识结合专业培训与合理执法，对于在保障研究诚信的同时促进AI效益至关重要。