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Evaluating large language models (LLMs) presents unique challenges. While automatic side-by-side evaluation, also known as LLM-as-a-judge, has become a promising solution, model developers and researchers face difficulties with scalability and ...

With text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks to mitigate the generation of offensive images. By focusing on “implicitly adversarial” prompts (those that ...

Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data practitioners often rely on dataset summaries, especially distributions of various derived features. Some features, like ...

As large language models (LLMs) become more advanced and impactful, it is increasingly important to scrutinize the data that they rely upon and produce. What is it to be a dataset practitioner doing this work? We approach this in two parts: first, we ...

Automatic side-by-side evaluation has emerged as a promising approach to evaluating the quality of responses from large language models (LLMs). However, analyzing the results from this evaluation approach raises scalability and interpretability ...

This paper presents VIVA, a novel interactive tool for visually exploring long videos and searching for specific moments. Previous work on video data exploration and analytics often assumes that manually-created, rich annotations are available. However, ...

Mobile video-based learning attracts many learners with its mobility and ease of access. However, most lectures are designed for desktops. Our formative study reveals mobile learners’ two major needs: more readable content and customizable video design. ...

Would you allow an AI agent to make decisions on your behalf? If the answer is “not always,” the next question becomes “in what circumstances”? Answering this question requires human users to be able to assess an AI agent—and not just with overall pass/...

This paper summarizes our endeavors in the past few years in terms of explaining image classifiers, with the aim of including negative results and insights we have gained. The paper starts with describing the explainable neural network (XNN), ...

This paper summarizes our endeavors explaining image classifiers, including XNN, I‐GOS, iGOS++ (a) and SAG(b). We present tricks of the trade to make explanations work and lessons learned from these research endeavors. image image

Saliency maps are popular tools for explaining the decisions of convolutional neural networks (CNNs) for image classification. Typically, for each image of interest, a single saliency map is produced, which assigns weights to pixels based on their ...

“In what circumstances would you want this AI to make decisions on your behalf?” We have been investigating how to enable a user of an Artificial Intelligence‐powered system to answer questions like this through a series of empirical studies, a ...

"In what circumstances would you want this Artificial Intelligence (AI) to make decisions on your behalf?" This paper summarizes multiple empirical investigations of how to enable a user of an AI‐powered system to answer questions like this. image image ...

The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep ...

Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks,...

Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily learn ...

The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques ...

We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help ...

Although researchers have devoted considerable attention to helping database users formulate queries, many users still find it challenging to specify queries that involve joining tables. To help users construct join queries for exploring relational ...

As complex machine learning systems become more widely adopted, it becomes increasingly challenging for users to understand models or interpret the results generated from the models. We present our ongoing work on developing interactive and visual ...

As the bulk electric grid becomes more complex, power system operators and engineers have more information to process and interpret than ever before. The information overload they experience can be mitigated by effective visualizations that facilitate ...