32 citations · 46 across the 9 of their papers we have counts for
9 papers
Limits of Emergent Reasoning of Large Language Models in Agentic Frameworks for Deterministic Games
Chris Su, Harrison Li, Matheus Marques +3
Recent work reports that Large Reasoning Models (LRMs) undergo a collapse in performance on solving puzzles beyond certain perplexity thresholds. In subsequent discourse, questions…
The Role of Visualization in LLM-Assisted Knowledge Graph Systems: Effects on User Trust, Exploration, and Workflows
Harry Li, Gabriel Appleby, Kenneth Alperin +2
Knowledge graphs (KGs) are powerful data structures, but exploring them effectively remains difficult for even expert users. Large language models (LLMs) are increasingly used to a…
Don't Just Translate, Agitate: Using Large Language Models as Devil's Advocates for AI Explanations
Ashley Suh, Kenneth Alperin, Harry Li +1
This position paper highlights a growing trend in Explainable AI (XAI) research where Large Language Models (LLMs) are used to translate outputs from explainability techniques, lik…
Mitigating LLM Hallucinations with Knowledge Graphs: A Case Study
Harry Li, Gabriel Appleby, Kenneth Alperin +2
High-stakes domains like cyber operations need responsible and trustworthy AI methods. While large language models (LLMs) are becoming increasingly popular in these domains, they s…
More Questions than Answers? Lessons from Integrating Explainable AI into a Cyber-AI Tool
Ashley Suh, Harry Li, Caitlin Kenney +2
We share observations and challenges from an ongoing effort to implement Explainable AI (XAI) in a domain-specific workflow for cybersecurity analysts. Specifically, we briefly des…
LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering
Harry Li, Gabriel Appleby, Ashley Suh
We present LinkQ, a system that leverages a large language model (LLM) to facilitate knowledge graph (KG) query construction through natural language question-answering. Traditiona…
A Preliminary Roadmap for LLMs as Assistants in Exploring, Analyzing, and Visualizing Knowledge Graphs
Harry Li, Gabriel Appleby, Ashley Suh
We present a mixed-methods study to explore how large language models (LLMs) can assist users in the visual exploration and analysis of knowledge graphs (KGs). We surveyed and inte…
ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems
Harry Li, Steven Jorgensen, John Holodnak +1
Recently, uncertainty-aware deep learning methods for multiclass labeling problems have been developed that provide calibrated class prediction probabilities and out-of-distributio…
Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities
Harry Li, Gabriel Appleby, Camelia Daniela Brumar +2
This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Thro…