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Found 4 papers, 1 papers with code
Language models align with human judgments on key grammatical constructions
Do Large Language Models (LLMs) make human-like linguistic generalizations?
Can We Distinguish Machine Learning from Human Learning?
Specifically, we suggest a large and extensible class of learning tasks, formulated as learning under rules.
Effects of Linguistic Labels on Learned Visual Representations in Convolutional Neural Networks: Labels matter!
We investigated the changes in visual representations learnt by CNNs when using different linguistic labels (e. g., trained with basic-level labels only, superordinate-level only, or both at the same time) and how they compare to human behavior when asked to select which of three images is most different.
Using Universal Dependencies in cross-linguistic complexity research
We evaluate corpus-based measures of linguistic complexity obtained using Universal Dependencies (UD) treebanks.
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