Hanjie Chen

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

2021 · arXiv (Cornell University)

Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, …

Pathologies of Pre-trained Language Models in Few-shot Fine-tuning

2022

Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance gain comes from. In this work, we propose to answer …

From Babbling to Fluency: Evaluating the Evolution of Language Models in Terms of Human Language Acquisition

2024 · arXiv (Cornell University)

We examine the language capabilities of language models (LMs) from the critical perspective of human language acquisition. Building on classical language development theories, we propose a three-stage framework to assess the abilities of LMs, ranging …

Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers

2020

To build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations. A new line of work on improving model interpretability has just started, …