About
Sarah Wiegreffe is an natural language processing and machine learning researcher and an assistant professor in the Department of Computer Science at the University of Maryland. She works on the explainability and interpretability of deep learning systems for language, with a focus on understanding how language models make predictions in order to make them more reliable, safe, and transparent to human users. She has been honored as a 3-time Rising Star in EECS, Machine Learning, and Generative AI. She was previously a postdoc at the Allen Institute for AI and the University of Washington and, before that, received her Ph.D. and M.S. degrees from Georgia Tech.
Research Performance Summary
First Recorded Paper
Explainable Prediction of Medical Codes from Clinical Text
Year: 2018
Citations: 848
Venue: North American Association for Computational Linguistics (NAACL) 2018
Latest Recorded Paper
Fine-Tune on the Format: First Improving Multiple-Choice Evaluation for Intermediate LLM Checkpoints
Year: 2025
Citations: 0
Venue: Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.