Search Results for author:
Found 20 papers, 10 papers with code
Interactive Prompt Debugging with Sequence Salience
We present Sequence Salience, a visual tool for interactive prompt debugging with input salience methods.
LLM Attributor: Interactive Visual Attribution for LLM 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.
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models.
Understanding the Dataset Practitioners Behind Large Language Model Development
We find that although data quality is a top priority, there is little consensus around what data quality is and how to evaluate it.
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models
Automatic side-by-side evaluation has emerged as a promising approach to evaluating the quality of responses from large language models (LLMs).
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
By focusing on ``implicitly adversarial'' prompts (those that trigger T2I models to generate unsafe images for non-obvious reasons), we isolate a set of difficult safety issues that human creativity is well-suited to uncover.
VLSlice: Interactive Vision-and-Language Slice Discovery
Recent work in vision-and-language demonstrates that large-scale pretraining can learn generalizable models that are efficiently transferable to downstream tasks.
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models
We present LinguisticLens, a novel inter-active visualization tool for making sense of and analyzing syntactic diversity of LLM-generated datasets.
Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL
Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios.
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps
In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML).
Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations
We describe a user interface and case study, where a small group of AI experts and developers attempt to identify reasoning flaws due to inaccurate agent learning.
From Heatmaps to Structural Explanations of Image Classifiers
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.
Contrastive Identification of Covariate Shift in Image Data
Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data.
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
Attention maps are a popular way of explaining the decisions of convolutional networks for image classification.
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Deep learning's great success motivates many practitioners and students to learn about this exciting technology.
CNN 101: Interactive Visual Learning for Convolutional Neural Networks
The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology.
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
We present FairVis, a mixed-initiative visual analytics system that integrates a novel subgroup discovery technique for users to audit the fairness of machine learning models.
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology.
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
We present a survey of the role of visual analytics in deep learning research, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where).
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge.
