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Found 5 papers, 4 papers with code
Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics
We found that probabilities represented by LMs were more likely to align with human judgments of being "tricked" by the NPI illusion which examines a structural dependency, compared to the comparative and the depth-charge illusions which require sophisticated semantic understanding.
Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning
We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models.
Discourse structure interacts with reference but not syntax in neural language models
Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations.
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
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 probable than an ungrammatical sentence).
