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：大型语言模型等生成式人工智能工具正在迅速改变学术研究和实际应用。然而，关于生成式AI在科学领域的伦理准则讨论仍然零散，凸显了建立基于共识的标准的迫切需求。本文提出一个初步框架，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、保密性和版权；在整合模型输出时避免抄袭和违反政策；确保应用带来整体利益；以及透明且可重复地使用AI。文中概述了常见场景以展示潜在的伦理违规行为。我们认为，全球共识、专业培训及合理执行对于在保障研究诚信的同时促进AI的益处至关重要。