Author name cluster

Zijie J. Wang

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5 papers

1 author row

AAAI Conference 2025 System Paper

LLM Attributor: Interactive Visual Attribution for LLM Generation

Seongmin Lee
Zijie J. Wang
Aishwarya Chakravarthy
Alec Helbling
ShengYun Peng
Mansi Phute
Duen Horng (Polo) Chau
Minsuk Kahng

While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We present LLM ATTRIBUTOR, a Python library that provides interactive visualizations for training data attribution of an LLM’s text generation. Our library offers a new way to quickly attribute an LLM’s text generation to training data points to inspect model behaviors, enhance its trustworthiness, and compare model-generated text with user-provided text. Thanks to LLM ATTRIBUTOR’s broad support for computational notebooks, users can easily integrate it into their workflow to interactively visualize attributions of their models.

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AAAI Conference 2025 System Paper

TRANSFORMER EXPLAINER: Interactive Learning of Text-Generative Models

Aeree Cho
Grace C. Kim
Alexander Karpekov
Alec Helbling
Zijie J. Wang
Seongmin Lee
Benjamin Hoover
Duen Horng (Polo) Chau

Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present TRANSFORMER EXPLAINER, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our tool helps users understand complex Transformer concepts by integrating a model overview and smooth transitions across abstraction levels of math operations and model structures. It runs a live GPT-2 model locally in the user’s browser, empowering users to experiment with their own input and observe in real-time how the internal components and parameters of the Transformer work together to predict the next tokens. 125,000 users have used our open-source tool at https://poloclub.github.io/ transformer-explainer/.

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IJCAI Conference 2024 Conference Paper

Interactive Visual Learning for Stable Diffusion

Seongmin Lee
Benjamin Hoover
Hendrik Strobelt
Zijie J. Wang
ShengYun Peng
Austin Wright
Kevin Li
Haekyu Park

Diffusion-based generative models’ impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pose challenges for non-experts to grasp. We introduce Diffusion Explainer, the first interactive visualization tool designed to elucidate how Stable Diffusion transforms text prompts into images. It tightly integrates a visual overview of Stable Diffusion’s complex components with detailed explanations of their underlying operations. This integration enables users to fluidly transition between multiple levels of abstraction through animations and interactive elements. Offering real-time hands-on experience, Diffusion Explainer allows users to adjust Stable Diffusion’s hyperparameters and prompts without the need for installation or specialized hardware. Accessible via users’ web browsers, Diffusion Explainer is making significant strides in democratizing AI education, fostering broader public access. More than 7, 200 users spanning 113 countries have used our open-sourced tool at https: //poloclub. github. io/diffusion-explainer/. A video demo is available at https: //youtu. be/MbkIADZjPnA.

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TMLR Journal 2023 Journal Article

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Aarohi Srivastava
Abhinav Rastogi
Abhishek Rao
Abu Awal Md Shoeb
Abubakar Abid
Adam Fisch
Adam R. Brown
Adam Santoro

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG- bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood develop- ment, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google- internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.

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AAAI Conference 2021 System Paper

SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models

Haekyu Park
Zijie J. Wang
Nilaksh Das
Anindya S. Paul
Pruthvi Perumalla
Zhiyan Zhou
Duen Horng Chau

Skeleton-based human action recognition technologies are increasingly used in video based applications, such as home robotics, healthcare on aging population, and surveillance. However, such models are vulnerable to adversarial attacks, raising serious concerns for their use in safety-critical applications. To develop an effective defense against attacks, it is essential to understand how such attacks mislead the pose detection models into making incorrect predictions. We present SKELETONVIS, the first interactive system that visualizes how the attacks work on the models to enhance human understanding of attacks.

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