Peter Hase

About Me

I am a third-year PhD student in the MURGe-Lab at the University of North Carolina at Chapel Hill, where I am advised by Mohit Bansal. My work at UNC is supported by a Google PhD Fellowship and previously by a Royster Fellowship. Before this, I graduated with a bachelor’s degree from Duke University, where my thesis advisor was Cynthia Rudin. At Duke I was supported by a Trinity Scholarship.

My research interests center on interpretable machine learning and natural language processing. I am particularly interested in techniques for explaining model behavior and aligning ML systems with human values. I see language models as a good object of study since we lack complete explanations for their behavior and human language provides a rich means of interaction with models. I am broadly interested in topics related to AI Safety; besides interpretable ML I have worked on methods for improving models via human explanations, providing recourses to people adversely affected by ML models, and making pretrained language models more truthful. In all of these areas, I find work on clarifying concepts and developing strong evaluation procedures especially valuable.

Email: peter@cs.unc.edu

News

2021 - New preprint on arxiv! “Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs” [pdf] [code]
2021 - Paper accepted to NeurIPS 2021! “Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals” [pdf] [code]
2021 - Awarded a Google PhD Fellowship for Natural Language Processing!
2021 - Invited talk at CHAI, UC Berkeley, on Evaluating Explainable AI
2021 - Paper accepted to EMNLP 2021: “FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging” [pdf] [code]
2021 - Named as an outstanding reviewer for ACL-IJCNLP 2021
2021 - New paper on arxiv! “Search Methods for Sufficient, Socially-Aligned Feature Importance Explanations with In-Distribution Counterfactuals” [pdf] [code]
2021 - Started summer internship at FAIR, supervised by Srini Iyer.
2021 - New blog post on the Alignment Forum: “Opinions on Interpretable Machine Learning and 70 Summaries of Recent Papers” [link]
2021 - New preprint on arxiv: “When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data” [pdf] [code]
2020 - New preprint on arxiv! “FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging” [pdf] [code]
2020 - Recognized as an Outstanding Reviewer for EMNLP 2020
2020 - Paper accepted into Findings of EMNLP, “Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?” [pdf] [code]
2020 - Paper accepted into ACL 2020, “Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?” [pdf] [code]
2019 - Paper accepted into AAAI-HCOMP 2019, “Interpretable Image Recognition with Hierarchical Prototypes” [pdf] [code]
2019 - Joined the UNC NLP lab
2019 - Graduated with a B.S. from the Department of Statistical Science at Duke University
2019 - Awarded the William R. Kenan Jr. (Royster) Fellowship from UNC Chapel Hill
2018 - Received First Prize in Darmouth’s PoetiX Literary Turing Test (see submission [pdf] and [code])
2018 - Nominated for Statistical Science Undergraduate TA of the Year