activity
20202024
most citedCan Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics

4 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

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…

cs.CL2023★ 4 cited

Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics

Yuhan Zhang, Edward Gibson, Forrest Davis

Language models (LMs) have been argued to overlap substantially with human beings in grammaticality judgment tasks. But when humans systematically make errors in language processin…

cs.CL2021

Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

Forrest Davis, Marten van Schijndel

A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in…

cs.CL2020

Discourse structure interacts with reference but not syntax in neural language models

Forrest Davis, Marten van Schijndel

Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by f…

cs.CL2020

Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment

Forrest Davis, Marten van Schijndel

A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more prob…