67 citations · 83 across the 8 of their papers we have counts for
9 papers
ADCanvas: Accessible and Conversational Audio Description Authoring for Blind and Low Vision Creators
Franklin Mingzhe Li, Michael Xieyang Liu, Cynthia L. Bennett +1
Audio Description (AD) provides essential access to visual media for blind and low vision (BLV) audiences. Yet current AD production tools remain largely inaccessible to BLV video…
Compass vs Railway Tracks: Unpacking User Mental Models for Communicating Long-Horizon Work to Humans vs. AI
Savvas Petridis, Michael Xieyang Liu, Alexander J. Fiannaca +2
As AI systems grow increasingly capable of operating for hours or days at a time, users' prompts are transforming into elaborate specifications for the AI to autonomously work on.…
What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts
Chenyang Yang, Yike Shi, Qianou Ma +3
Prompt underspecification is a common challenge when interacting with LLMs. In this paper, we present an in-depth analysis of this problem, showing that while LLMs can often infer…
AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages
Muzhe Wu, Yanzhi Zhao, Shuyi Han +2
Responsible AI (RAI) efforts increasingly emphasize the importance of addressing potential harms early in the AI development lifecycle through social-technical lenses. However, in…
Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning
Michael Xieyang Liu, Savvas Petridis, Vivian Tsai +4
Multimodal large language models (MLLMs), with their expansive world knowledge and reasoning capabilities, present a unique opportunity for end-users to create personalized AI sens…
The Evolution of LLM Adoption in Industry Data Curation Practices
Crystal Qian, Michael Xieyang Liu, Emily Reif +7
As large language models (LLMs) grow increasingly adept at processing unstructured text data, they offer new opportunities to enhance data curation workflows. This paper explores t…
In Situ AI Prototyping: Infusing Multimodal Prompts into Mobile Settings with MobileMaker
Savvas Petridis, Michael Xieyang Liu, Alexander J. Fiannaca +3
Recent advances in multimodal large language models (LLMs) have made it easier to rapidly prototype AI-powered features, especially for mobile use cases. However, gathering early,…
"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output
Michael Xieyang Liu, Frederick Liu, Alexander J. Fiannaca +4
Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow…
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models
Minsuk Kahng, Ian Tenney, Mahima Pushkarna +7
Automatic side-by-side evaluation has emerged as a promising approach to evaluating the quality of responses from large language models (LLMs). However, analyzing the results from…