2025.07.28 Looper: 大模型时代是否还有必要进行手工写作是一个问题，又慢又笨，远不及大模型带来的巨量输出，时代变革像巨浪袭来，而我们为什么坚持要手工写作，一直是一个问题，Nature发了一篇文章讲述了大语言模型时代的人类创作价值。我个人一直认为写作是一个系统工程，不是华丽辞藻和高超的笔法，更多的是一种基于逻辑的精确，正如那句格言所说：阅读使人丰富，交谈使人聪明，写作使人精确。
2025.06.16 Nature | 论在大语言模型时代人类创作的科学写作的价值
撰写科学论文是科学方法的有机组成部分，也是传播研究成果的常见做法。然而，写作不只是报告结果，它还为人们提供了一种发现新思想和新观点的工具。写作迫使我们进行思考 —— 不是以我们大脑通常那种混乱、非线性的方式漫游，而是以一种结构化、有意识的方式思考。通过写下来，我们可以将多年的研究、数据和分析整理成一个完整的故事，从而确定我们的核心信息以及我们工作的影响力。这不仅仅是一种哲学观点，还有科学证据的支持。例如，手写可以促进广泛的大脑连接，并且对学习和记忆有积极影响。
这是一个呼吁，呼吁我们继续认识到人类创作的科学写作的重要性。
在大语言模型时代，这一呼吁可能显得不合时宜，因为只要有合适的提示，大语言模型几分钟内就能生成完整的科学论文 2（和同行评审报告 3），似乎在完成艰巨的研究工作后，能节省发布成果的时间和精力。然而，大语言模型不能被视为作者，因为它们缺乏责任感，因此，我们不会考虑发表完全由大语言模型撰写的手稿（使用大语言模型进行编辑是允许的，但应予以声明）。重要的是，如果写作即思考，那么我们阅读的难道不是大语言模型的 “思想”，而不是论文背后研究人员的思想吗？
当前的大语言模型也可能会出错，这种现象被称为 “幻觉” 4。因此，大语言模型生成的文本需要经过彻底的检查和验证（包括每一个参考文献，因为它们可能是编造的 5）。由此可见，当前的大语言模型究竟能节省多少时间，还是个未知数。编辑大语言模型生成的文本可能比从头撰写一篇文章或同行评审报告更困难、更耗时，部分原因是人们需要理解其中的推理过程才能进行编辑。冯林・刘（Fenglin Liu）及其团队在本期的一篇综述文章中概述了一些仅基于科学数据库训练的大语言模型，或许能解决其中一些问题。时间会证明一切。
这并不是说大语言模型不能作为科学写作中的宝贵工具。例如，大语言模型可以帮助提高文本的可读性和语法水平，这对那些英语不是母语的人来说可能特别有用。大语言模型还可能在搜索和总结各种科学文献方面很有价值 6，它们可以提供要点并协助构思想法。此外，大语言模型还有助于克服写作障碍，为研究结果提供替代解释，或识别看似不相关学科之间的联系，从而激发新的想法。
然而，将整个写作过程外包给大语言模型可能会使我们失去反思自己所在领域的机会，也会使我们无法参与到将研究成果塑造成一个引人入胜的叙述这一创造性的重要工作中 —— 而这一技能在学术写作和出版之外无疑也是很重要的。
Nature | Writing is thinking
https://www.nature.com/articles/s44222-025-00323-4
On the value of human-generated scientific writing in the age of large-language models.
Writing scientific articles is an integral part of the scientific method and common practice to communicate research findings. However, writing is not only about reporting results; it also provides a tool to uncover new thoughts and ideas. Writing compels us to think — not in the chaotic, non-linear way our minds typically wander, but in a structured, intentional manner. By writing it down, we can sort years of research, data and analysis into an actual story, thereby identifying our main message and the influence of our work. This is not merely a philosophical observation; it is backed by scientific evidence. For example, handwriting can lead to widespread brain connectivity1 and has positive effects on learning and memory.
This is a call to continue recognizing the importance of human-generated scientific writing.
This call may seem anachronistic in the age of large-language models (LLMs), which, with the right prompts, can create entire scientific articles2 (and peer-review reports3) in a few minutes, seemingly saving time and effort in getting results out once the hard research work is done. However, LLMs are not considered authors as they lack accountability, and thus, we would not consider publishing manuscripts written entirely by LLMs (using LLMs for copy-editing is allowed but should be declared). Importantly, if writing is thinking, are we not then reading the ‘thoughts’ of the LLM rather than those of the researchers behind the paper?
Current LLMs might also be wrong, a phenomenon called hallucination4. Therefore, LLM-generated text needs to be thoroughly checked and verified (including every reference as it might be made up5). It thus remains questionable how much time current LLMs really save. It might be more difficult and time-consuming to edit an LLM-generated text than to write an article or peer-review report from scratch, partly because one needs to understand the reasoning to be able to edit it. Some of these issues might be addressed by LLMs trained only on scientific databases, such as those outlined in a Review article by Fenglin Liu and team in this issue. Time will tell.
All that is not to say LLMs cannot serve as valuable tools in scientific writing. For example, LLMs can aid in improving readability and grammar, which might be particularly useful to those for which English is not their first language. LLMs might also be valuable for searching and summarizing diverse scientific literature6, and they can provide bullet points and assist in the brainstorming of ideas. In addition, LLMs can be beneficial in overcoming writer’s block, provide alternative explanations for findings or identify connections between seemingly unrelated subjects, thereby sparking new ideas.
Nevertheless, outsourcing the entire writing process to LLMs may deprive us of the opportunity to reflect on our field and engage in the creative, essential task of shaping research findings into a compelling narrative — a skill that is certainly important beyond scholarly writing and publishing.