20 citations · 41 across the 10 of their papers we have counts for
5 papers · 1 filter
CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents
Jeongeun Park, Seungwon Lim, Joonhyung Lee +4
In this paper, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models…
LMCanvas: Object-Oriented Interaction to Personalize Large Language Model-Powered Writing Environments
Tae Soo Kim, Arghya Sarkar, Yoonjoo Lee +2
Large language models (LLMs) can enhance writing by automating or supporting specific tasks in writers' workflows (e.g., paraphrasing, creating analogies). Leveraging this capabili…
A Word is Worth a Thousand Pictures: Prompts as AI Design Material
Chinmay Kulkarni, Stefania Druga, Minsuk Chang +3
Recent advances in Machine-Learning have led to the development of models that generate images based on a text description.Such large prompt-based text to image models (TTIs), trai…
The Prompt Artists
Minsuk Chang, Stefania Druga, Alex Fiannaca +4
This paper examines the art practices, artwork, and motivations of prolific users of the latest generation of text-to-image models. Through interviews, observations, and a user sur…
Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts
Dongyoon Han, Junsuk Choe, Seonghyeok Chun +5
Supervised learning of image classifiers distills human knowledge into a parametric model through pairs of images and corresponding labels (X,Y). We argue that this simple and wide…