Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advent of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and over-hyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices.

翻译：大型语言模型（LLMs）正日益融入科学工作流程中。然而，我们仍未充分理解这种整合所带来的影响。大型语言模型的出现应如何影响科学实践？在这篇观点文章中，我们邀请了四组不同的科学家就这一问题进行反思，分享他们的观点并展开辩论。Schulz等人认为，与LLMs合作本质上与人类合作者并无不同；而Bender等人则认为，LLMs常被误用和过度炒作，其局限性说明应更专注于专业性强、易于解释的工具。Marelli等人强调了透明归属和负责任使用LLMs的重要性。最后，Botvinick和Gershman主张，人类应保留制定科学路线图的责任。为促进讨论，四组观点均附有来自各组的回应。通过将这些不同观点进行对话，我们旨在引起学术界对采用LLMs及其对当前和未来科学实践影响的重要考量。