5 papers
Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation
Aeree Cho, Grace C. Kim, Alexander Karpekov +6
The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications. However, its complexity makes it difficult for no…
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…
ClickDiffusion: Harnessing LLMs for Interactive Precise Image Editing
Alec Helbling, Seongmin Lee, Polo Chau
Recently, researchers have proposed powerful systems for generating and manipulating images using natural language instructions. However, it is difficult to precisely specify many…
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…