Large language models (LLMs) are becoming more prevalent and have found a
ubiquitous use in providing different forms of writing assistance. However,
LLM-powered writing systems can frustrate users due to their limited
personalization and control, which can be exacerbated when users lack
experience with prompt engineering. We see design as one way to address these
challenges and introduce GhostWriter, an AI-enhanced writing design probe where
users can exercise enhanced agency and personalization. GhostWriter leverages
LLMs to learn the user's intended writing style implicitly as they write, while
allowing explicit teaching moments through manual style edits and annotations.
We study 18 participants who use GhostWriter on two different writing tasks,
observing that it helps users craft personalized text generations and empowers
them by providing multiple ways to control the system's writing style. From
this study, we present insights regarding people's relationship with
AI-assisted writing and offer design recommendations for future work.

GhostWriter 是一种使用大型语言模型 (LLM) 进行人工智能增强的写作设计探针，用户可以在写作过程中隐式地学习其预期写作风格，并通过手动样式编辑和注释实现显式教学，研究结果表明 GhostWriter 帮助用户个性化生成文本，通过提供多种方式控制系统的写作风格增强用户的能力，为未来的工作提供了设计建议。

GhostWriter：通过个性化和代理增强协作人工智能写作体验

GhostWriter: Augmenting Collaborative Human-AI Writing Experiences  Through Personalization and Agency

The development of Natural Language Generation models has led to the creation
of powerful Artificial Intelligence-assisted writing tools. These tools are
capable of predicting users' needs and actively providing suggestions as they
write. In this work, we conduct a comparative user-study between such tools
from an information retrieval lens: pull and push. Specifically, we investigate
the user demand of AI-assisted writing, the impact of the two paradigms on
quality, ownership of the writing product, and efficiency and enjoyment of the
writing process. We also seek to understand the impact of bias of AI-assisted
writing. Our findings show that users welcome seamless assistance of AI in
their writing. Furthermore, AI helped users to diversify the ideas in their
writing while keeping it clear and concise more quickly. Users also enjoyed the
collaboration with AI-assisted writing tools and did not feel a lack of
ownership. Finally, although participants did not experience bias in our
experiments, they still expressed explicit and clear concerns that should be
addressed in future AI-assisted writing tools.

研究了自然语言生成技术在人工智能辅助写作工具中的应用。通过信息检索的角度从 “拔式” 和 “推式” 两个范式进行比较用户研究，以了解 AI 辅助写作的用户需求、对写作质量、所有权、写作过程的效率和愉悦度的影响，以及 AI 偏见的影响。研究发现用户欢迎 AI 在他们的写作中提供无缝协助，并且 AI 在保持写作清晰简洁的同时帮助用户使其写作思路更加丰富，用户也享受与 AI 合作。尽管参与者在实验中没有经历到偏见，但他们仍然表达了明确的担忧，这应在未来的 AI 辅助写作工具中加以解决。

AI 辅助写作的未来

The Future of AI-Assisted Writing

While large-scale pretrained language models have significantly improved
writing assistance functionalities such as autocomplete, more complex and
controllable writing assistants have yet to be explored. We leverage advances
in language modeling to build an interactive writing assistant that generates
and rephrases text according to fine-grained author specifications. Users
provide input to our Intent-Guided Assistant (IGA) in the form of text
interspersed with tags that correspond to specific rhetorical directives (e.g.,
adding description or contrast, or rephrasing a particular sentence). We
fine-tune a language model on a dataset heuristically-labeled with author
intent, which allows IGA to fill in these tags with generated text that users
can subsequently edit to their liking. A series of automatic and crowdsourced
evaluations confirm the quality of IGA's generated outputs, while a small-scale
user study demonstrates author preference for IGA over baseline methods in a
creative writing task. We release our dataset, code, and demo to spur further
research into AI-assisted writing.

基于预训练语言模型的交互式写作助手 IGA，可以通过标记进行 fine-grained 的文本生成和改写，在自动和人工评估以及小规模用户研究中表现良好，同时公开了数据集、代码和演示。