While LLMs now enable social media users to create content classifiers easily through natural language, automatic prompt optimization techniques are often necessary to create performant classifiers. However, such techniques can fail to consider how users want to evolve their classifiers over the course of usage, including desiring to steer them in different ways during initialization and refinement. We introduce a user-centered prompt optimization technique, Promptimizer, that maintains high performance and ease-of-use but additionally (1) allows for user input into the optimization process and (2) produces final prompts that are interpretable. A lab experiment (n=16) found that users significantly preferred Promptimizer’s human-in-the-loop optimization over a fully automatic approach. We also implement Promptimizer into Puffin, a tool to support YouTube content creators in creating and maintaining personal classifiers to manage their comments. Over a 3-week deployment with 10 creators, participants successfully created diverse filters to better understand their audiences and protect their communities.
ACM CHI Conference on Human Factors in Computing Systems