5 citations · 7 across the 6 of their papers we have counts for
6 papers
Representing Visualization Insights as a Dense Insight Network
Jane Hoffswell, Victor Soares Bursztyn, Shunan Guo +2
We propose a dense insight network framework to encode the relationships between automatically generated insights from a complex dashboard based on their shared characteristics. Ou…
How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts
Huichen Will Wang, Jane Hoffswell, Sao Myat Thazin Thane +2
Large Language Models (LLMs) have been adopted for a variety of visualizations tasks, but how far are we from perceptually aware LLMs that can predict human takeaways? Graphical pe…
Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models
Victor S. Bursztyn, David Demeter, Doug Downey +1
How to usefully encode compositional task structure has long been a core challenge in AI. Recent work in chain of thought prompting has shown that for very large neural language mo…
Gaudí: Conversational Interactions with Deep Representations to Generate Image Collections
Victor S. Bursztyn, Jennifer Healey, Vishwa Vinay
Based on recent advances in realistic language modeling (GPT-3) and cross-modal representations (CLIP), Gaudí was developed to help designers search for inspirational images using…
"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems
Victor S. Bursztyn, Jennifer Healey, Nedim Lipka +3
Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g.,…
Developing a Conversational Recommendation System for Navigating Limited Options
Victor S. Bursztyn, Jennifer Healey, Eunyee Koh +2
We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice. Unlike many internet scale system…