New study finds LMs show some human-like language learning biases

A new research paper explores whether language models (LMs) can offer insights into human language learning by training them on typologically unattested languages. The study, which focused on 12 languages and used GPT-2 small, found that while the model could largely distinguish between attested and impossible languages, it did not achieve perfect separation. Furthermore, GPT-2 small's performance on generalization tests indicated some human-like inductive biases, though these were less pronounced than in human learners. AI

IMPACT Suggests LMs may exhibit some human-like inductive biases in language acquisition, though less pronounced than in humans.

RANK_REASON The cluster contains an academic paper published on arXiv detailing research findings on language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• GPT-2 small
• Greenberg's Universal 20
• Xiulin Yang

• paper
• model release

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