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

New York University, New York NY

Investigators

Abstract

Generative artificial intelligence (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 variety 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 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 content variety. 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.