20 citations · 41 across the 10 of their papers we have counts for
10 papers
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…
Leveraging Pre-Trained Language Models to Streamline Natural Language Interaction for Self-Tracking
Young-Ho Kim, Sungdong Kim, Minsuk Chang +1
Current natural language interaction for self-tracking tools largely depends on bespoke implementation optimized for a specific tracking theme and data format, which is neither gen…
ClaimDiff: Comparing and Contrasting Claims on Contentious Issues
Miyoung Ko, Ingyu Seong, Hwaran Lee +3
With the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence. However, canonical fact verification tasks do…
ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO
Sanghyuk Chun, Wonjae Kim, Song Park +2
Image-Text matching (ITM) is a common task for evaluating the quality of Vision and Language (VL) models. However, existing ITM benchmarks have a significant limitation. They have…
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
Boseop Kim, HyoungSeok Kim, Sang-Woo Lee +34
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less report…
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation
Sungdong Kim, Minsuk Chang, Sang-Woo Lee
We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generate…