NLI autonomously discovers a vocabulary of primitive operations and interprets variable-length programs via a neural executor, allowing end-to-end training and gradient-based test-time adaptation that outperforms prior methods on combinatorial generalization tasks.
arXiv preprint arXiv:2002.01365 , year=
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Multi-agent simulations with naturalistic lexicons and phonological rules show scale-free networks and Bernoulli adoption produce more plausible morphologies, evaluated by an LLM historical linguist debate system and tested via historical case studies.
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Gradient-Based Program Synthesis with Neurally Interpreted Languages
NLI autonomously discovers a vocabulary of primitive operations and interprets variable-length programs via a neural executor, allowing end-to-end training and gradient-based test-time adaptation that outperforms prior methods on combinatorial generalization tasks.
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Agent-based models for the evolution of morphological alternation patterns
Multi-agent simulations with naturalistic lexicons and phonological rules show scale-free networks and Bernoulli adoption produce more plausible morphologies, evaluated by an LLM historical linguist debate system and tested via historical case studies.