Critique Challenges Bayesian Prior Distillation in ANNs for Language Learning.

This paper offers a critical commentary on a previous study by McCoy & Griffiths (M&G, 2025), which proposed that Bayesian priors could be distilled into Artificial Neural Networks (ANNs) using Model-Agnostic Meta-Learning (MAML) to achieve rapid language learning. M&G had presented empirical evidence suggesting that meta-trained networks could match the formal language learning abilities of Bayesian learners, outperforming standard ANNs. However, the authors of this comment argue that M&G's procedure, under the standard interpretation of a prior, does not actually instill a Bayesian prior into the ANNs. Instead, they contend that it merely provides a favorable initialization of network weights without altering the fundamental objective function. The commentary further explores a more permissive interpretation where the entire system might be seen as a Bayesian learner but highlights significant challenges with this view. Finally, the authors demonstrate that M&G's model, unlike genuine Bayesian learners, exhibits overfitting and poor generalization to unseen data, casting doubt on its claim of approximating Bayesian learning effectively.