1 citations · 2 across the 6 of their papers we have counts for
6 papers
Zero-Shot Multi-Hop Question Answering via Monte-Carlo Tree Search with Large Language Models
Seongmin Lee, Jaewook Shin, Youngjin Ahn +3
Recent advances in large language models (LLMs) have significantly impacted the domain of multi-hop question answering (MHQA), where systems are required to aggregate information a…
Interactive Visual Learning for Stable Diffusion
Seongmin Lee, Benjamin Hoover, Hendrik Strobelt +7
Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pos…
LLM Attributor: Interactive Visual Attribution for LLM Generation
Seongmin Lee, Zijie J. Wang, Aishwarya Chakravarthy +5
While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the imp…
Point and Instruct: Enabling Precise Image Editing by Unifying Direct Manipulation and Text Instructions
Alec Helbling, Seongmin Lee, Polo Chau
Machine learning has enabled the development of powerful systems capable of editing images from natural language instructions. However, in many common scenarios it is difficult for…
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models
Justin Blalock, David Munechika, Harsha Karanth +4
The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes. Traditional try-on methods grapple with chal…
VisGrader: Automatic Grading of D3 Visualizations
Matthew Hull, Vivian Pednekar, Hannah Murray +9
Manually grading D3 data visualizations is a challenging endeavor, and is especially difficult for large classes with hundreds of students. Grading an interactive visualization req…