110 citations · 155 across the 8 of their papers we have counts for
12 papers
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes
Mycal Tucker, Tiwalayo Eisape, Peng Qian +2
Recent causal probing literature reveals when language models and syntactic probes use similar representations. Such techniques may yield "false negative" causality results: models…
Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models
Yiwen Wang, Jennifer Hu, Roger Levy +1
Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement. However, it remains u…
Structural Guidance for Transformer Language Models
Peng Qian, Tahira Naseem, Roger Levy +1
Transformer-based language models pre-trained on large amounts of text data have proven remarkably successful in learning generic transferable linguistic representations. Here we s…
What if This Modified That? Syntactic Interventions via Counterfactual Embeddings
Mycal Tucker, Peng Qian, Roger Levy
Neural language models exhibit impressive performance on a variety of tasks, but their internal reasoning may be difficult to understand. Prior art aims to uncover meaningful prope…
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
Ethan Wilcox, Peng Qian, Richard Futrell +3
Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We asse…
On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior
Ethan Gotlieb Wilcox, Jon Gauthier, Jennifer Hu +2
Human reading behavior is tuned to the statistics of natural language: the time it takes human subjects to read a word can be predicted from estimates of the word's probability in…