A Unified Assessment of the Poverty of the Stimulus Argument for Neural Language Models

Abstract

Several recent contributions have evaluated the Poverty of the Stimulus Hypothesis (PoSH) using Artificial Neural Networks (ANNs). The results suggest that ANN-based language models can acquire certain structure-dependent generalizations from limited input without structural inductive biases of the type traditionally hypothesized by linguists. However, existing studies have largely focused on individual phenomena and adopted different evaluation protocols, leaving it unclear whether previous findings generalize across phenomena and learning conditions. We introduce \poshbench, a unified benchmark covering four canonical PoS phenomena. Training Transformer, LSTM, and n-gram models, we find that ANN-based models can achieve above-chance generalization from surprisingly limited input (10M words), but they show less efficient learning than children as input scale grows. Moreover, cognitively motivated inductive biases substantially improve broad syntactic competence but do not consistently translate to PoS-relevant generalization. Our findings provide systematic evidence challenging the claim that innate syntax is the only possible route to generalization, while suggesting that human-like learning efficiency requires inductive biases beyond those implemented here.