Tomas Pfister

RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling

2019 · arXiv (Cornell University)

Understanding black-box machine learning models is important towards their widespread adoption. However, developing globally interpretable models that explain the behavior of the entire model is challenging. An alternative approach is to explain black-box models through …

Data-Efficient and Interpretable Tabular Anomaly Detection

2022 · arXiv (Cornell University)

Anomaly detection (AD) plays an important role in numerous applications. We focus on two understudied aspects of AD that are critical for integration into real-world applications. First, most AD methods cannot incorporate labeled data that …

Better Zero-Shot Reasoning with Self-Adaptive Prompting

2023 · arXiv (Cornell University)

Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible by their strong few and zero-shot abilities -- they can effectively learn …

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

2023

Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister. Findings of the Association for Computational Linguistics: ACL 2023. 2023.