CAREER: Enhancing Diversity and Personalization in Human-AI Collaborative Writing

大型语言模型（LLM）等生成式AI技术正在迅速改变人们创建内容的方式。通过起草，编辑和建议文本，基于法学硕士的写作助理有可能提高写作质量和提高作者的生产力。然而，由于数百万用户依赖于相同的底层模型来生成文本，因此存在创建重复内容的潜在风险-导致内容相似性增加和内容多样性的总体减少。该项目旨在衡量基于法学硕士的写作助理对内容多样性的影响，并为下一代写作助理开发增强（而不是取代）个人声音的方法。除了技术贡献外，该项目还将为社会科学家和政策制定者提供有关监管生成性人工智能技术的见解和最佳实践。本研究的成果也将通过教学和研究活动整合到本科和研究生的学习中。首先，研究人员的目标是通过量化共同写作如何在个人属性和整体多样性方面改变所产生的内容来了解与LLM一起写作的意外影响。基于从这项调查中获得的见解，下一个目标是通过探索促进以人为本的写作助手的计算方法来解决所确定的问题。主要的方法包括微调LLM与多样性意识的目标和适应LLM在线学习和适合每个用户的偏好在写作过程中。总的来说，这个项目将产生指标，数据集和方法，有助于更多的以人为本的写作援助。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Generative AI technologies like large language models (LLMs) are rapidly changing how people create content. By drafting, editing, and suggesting text, LLM-based writing assistants have the potential to improve writing quality and increase author productivity. However, as millions of users rely on the same underlying model to produce text, there is a potential risk of homogenizing content creation - resulting in increased content similarity and an overall reduction in content diversity. This project aims to measure the impact of LLM-based writing assistants on content diversity and develop methods for the next-generation writing assistants that enhance (as opposed to replace) personal voices. Aside from the technical contributions, this project will provide insights and best practices to social scientists and policymakers on regulating generative AI technologies. The result of this research will also be integrated in undergraduate and graduate studies through both teaching and research activities.The proposed research activities consist of three directions. First, the researchers aim to understand the unintended effects of writing with LLMs by quantifying how co-writing alters the produced content in terms of personal attributes and overall diversity. Building upon the insights gained from this investigation, the next objective is to address the identified issues by exploring computational methods that promote human-centered writing assistants. The main approaches include finetuning LLMs with a diversity-aware objective and adapting LLMs online to learn and suit each user's preference during writing. Overall, this project will produce metrics, datasets, and methods that contribute to more human-centered writing assistance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.