16 citations · 29 across the 11 of their papers we have counts for
12 papers
Too Long to Watch, Too Short to Leave: Designing for Agent Micro-Waits
Kevin Pu, Anh Truong, Michael Terry +3
Agentic systems increasingly carry out complex work, where a single turn routinely runs for minutes. This produces a micro-wait that is too long to watch and too short to leave. In…
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.…
Facilitating Longitudinal Interaction Studies of AI Systems
Tao Long, Sitong Wang, Émilie Fabre +14
UIST researchers develop tools to address user challenges. However, user interactions with AI evolve over time through learning, adaptation, and repurposing, making one time evalua…
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…
Position: Towards Bidirectional Human-AI Alignment
Hua Shen, Tiffany Knearem, Reshmi Ghosh +21
Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes…
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,…
ConstitutionalExperts: Training a Mixture of Principle-based Prompts
Savvas Petridis, Ben Wedin, Ann Yuan +2
Large language models (LLMs) are highly capable at a variety of tasks given the right prompt, but writing one is still a difficult and tedious process. In this work, we introduce C…
PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows
Savvas Petridis, Michael Terry, Carrie J. Cai
Prototyping AI applications is notoriously difficult. While large language model (LLM) prompting has dramatically lowered the barriers to AI prototyping, designers are still protot…
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles
Savvas Petridis, Ben Wedin, James Wexler +5
Large language model (LLM) prompting is a promising new approach for users to create and customize their own chatbots. However, current methods for steering a chatbot's outputs, su…
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models
Emily Reif, Minsuk Kahng, Savvas Petridis
Large language models (LLMs) can be used to generate smaller, more refined datasets via few-shot prompting for benchmarking, fine-tuning or other use cases. However, understanding…
PopBlends: Strategies for Conceptual Blending with Large Language Models
Sitong Wang, Savvas Petridis, Taeahn Kwon +2
Pop culture is an important aspect of communication. On social media people often post pop culture reference images that connect an event, product or other entity to a pop culture…
Lightweight Decoding Strategies for Increasing Specificity
Katy Ilonka Gero, Chris Kedzie, Savvas Petridis +1
Language models are known to produce vague and generic outputs. We propose two unsupervised decoding strategies based on either word-frequency or point-wise mutual information to i…