Analyzing the Linguistic Priors of Language Models with Synthetic Languages

AbstractWhile modern language model architectures are often assumed to be language-agnostic, there is limited evidence as to whether these models actually process the wide diversity of natural languages equally well. We investigate this question by analyzing how well LMs learn carefully constructed artificial languages containing a variety of verbal complexity, ranging from simple paradigms to covering far more verb classes than occur in natural languages. Rather than learning all languages equally efficiently, models trained on these languages show strict preferences for processing simpler languages. Furthermore, while some observed behaviors mimic human linguistic priors, we find that they indicate the model memorizes its training data rather than generalizes from it.