Large language models have introduced exciting new opportunities and challenges in designing and developing new AI-assisted writing support tools. Recent work has shown that leveraging this new technology can transform writing in many scenarios such as ideation during creative writing, editing support, and summarization. However, AI-supported expository writing--including real-world tasks like scholars writing literature reviews or doctors writing progress notes--is relatively understudied. In this position paper, we argue that developing AI supports for expository writing has unique and exciting research challenges and can lead to high real-world impacts. We characterize expository writing as evidence-based and knowledge-generating: it contains summaries of external documents as well as new information or knowledge. It can be seen as the product of authors' sensemaking process over a set of source documents, and the interplay between reading, reflection, and writing opens up new opportunities for designing AI support. We sketch three components for AI support design and discuss considerations for future research.
翻译:大型语言模型为设计和开发新的人工智能辅助写作支持工具带来了令人兴奋的新机遇和挑战。最近的研究表明,利用这种新技术可以在许多场景下转化写作,例如创意写作过程中的构思、编辑支持和总结。然而,人工智能支持的说明性写作,包括学者撰写文献评论或医生撰写进度记录等真实世界任务,相对研究较少。在这篇立场论文中,我们认为为说明性写作开发人工智能支持具有独特而令人兴奋的研究挑战,并可能产生高度现实世界的影响。我们将说明性写作具体化为基于证据的知识生成:它包含了外部文献的摘要以及新的信息或知识。它可以被视为作者在一组来源文档上进行意义构建过程的产物,阅读、反思和写作之间的相互作用为人工智能支持的设计打开了新的机遇。我们概述了人工智能支持设计的三个组成部分,并讨论了未来研究的注意事项。