3 papers
cs.LG2025
It's LIT! Reliability-Optimized LLMs with Inspectable Tools
Ruixin Zhang, Jon Donnelly, Zhicheng Guo +4
Large language models (LLMs) have exhibited remarkable capabilities across various domains. The ability to call external tools further expands their capability to handle real-world…
cs.LG2025
"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts
Varun Babbar, Zhicheng Guo, Cynthia Rudin
The performance of machine learning models relies heavily on the quality of input data, yet real-world applications often face significant data-related challenges. A common issue a…
cs.CV2025
Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
Jon Donnelly, Zhicheng Guo, Alina Jade Barnett +3
Interpretability is critical for machine learning models in high-stakes settings because it allows users to verify the model's reasoning. In computer vision, prototypical part mode…