Papers by Filippo Ficarra

1 papers
A Distributional Perspective on Word Learning in Neural Language Models (2025.naacl-long)

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Challenge: Language models are increasingly being studied as models of human language learners.
Approach: They propose a distributional approach to word learning that captures distributional knowledge and gradient preferences for the word’s appropriateness.
Outcome: The proposed signatures capture knowledge of where the target word can and cannot occur as well as gradient preferences about the word’s appropriateness.

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