GPT-2 models show mixed success replicating human linguistic patterns

Researchers investigated how language models handle differential argument marking (DAM), a linguistic feature where marking depends on semantic prominence. Using GPT-2 models trained on synthetic data, they found that models could replicate human-like preferences for natural markedness directions, favoring systems where overt marking targets semantically atypical arguments. However, the models did not reproduce the human tendency to more frequently mark objects over subjects in DAM systems, suggesting different typological tendencies may stem from distinct origins. AI

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IMPACT Reveals nuances in how LLMs process linguistic structures, suggesting distinct underlying mechanisms for different typological features.

RANK_REASON Academic paper detailing experimental results on language model behavior. [lever_c_demoted from research: ic=1 ai=1.0]

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• GPT-2
• Iskar Deng

• paper
• other