1 citations · 1 across the 6 of their papers we have counts for
7 papers
Training an NLP Scholar at a Small Liberal Arts College: A Backwards Designed Course Proposal
Grusha Prasad, Forrest Davis
The rapid growth in natural language processing (NLP) over the last couple years has generated student interest and excitement in learning more about the field. In this paper, we p…
SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser
Grusha Prasad, Tal Linzen
Structural priming is a widely used psycholinguistic paradigm to study human sentence representations. In this work we introduce SPAWN, a cognitively motivated parser that can gene…
Can training neural language models on a curriculum with developmentally plausible data improve alignment with human reading behavior?
Aryaman Chobey, Oliver Smith, Anzi Wang +1
The use of neural language models to model human behavior has met with mixed success. While some work has found that the surprisal estimates from these models can be used to predic…
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction
Shauli Ravfogel, Grusha Prasad, Tal Linzen +1
When language models process syntactically complex sentences, do they use their representations of syntax in a manner that is consistent with the grammar of the language? We propos…
Dynabench: Rethinking Benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie +16
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop datase…
To what extent do human explanations of model behavior align with actual model behavior?
Grusha Prasad, Yixin Nie, Mohit Bansal +3
Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior. Using Natur…
Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models
Grusha Prasad, Marten van Schijndel, Tal Linzen
Neural language models (LMs) perform well on tasks that require sensitivity to syntactic structure. Drawing on the syntactic priming paradigm from psycholinguistics, we propose a n…