arXiv subjects

Kathryn Yurechko

Publications and source records attributed to Kathryn Yurechko.

2 recordsLinked to original sources

arXiv2025

Promptimizer: User-Led Prompt Optimization for Personal Content Classification

While LLMs now enable 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 social media users want to evolve their filters over the course of usage, including desiring to steer them in different ways during initialization and iteration. 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 further 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.

cs.HC↗

arXiv2025

End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Prompting

Existing tools for laypeople to create personal classifiers often assume a motivated user working uninterrupted in a single, lengthy session. However, users tend to engage with social media casually, with many short sessions on an ongoing, daily basis. To make creating personal classifiers for content curation easier for such users, tools should support rapid initialization and iterative refinement. In this work, we compare three strategies -- (1) example labeling, (2) rule writing, and (3) large language model (LLM) prompting -- for end users to build personal content classifiers. From an experiment with 37 non-programmers tasked with creating personalized moderation filters, we found that participants preferred different initializing strategies in different contexts, despite LLM prompting's better performance. However, all strategies faced challenges with iterative refinement. To overcome challenges in iterating on their prompts, participants even adopted hybrid approaches such as providing examples as in-context examples or writing rule-like prompts.

cs.HC↗