R4 suggested that in the continual learning setup we train until the model overfits, and report results on a held-out33 set. We set up the experiment in Section 4.2 to mimic naturalistic life-long settings where some classes are seen34 morefrequentlythanothers.

Studies with impossible languages falsify LMs as models of human language Jeffrey S. Bowers, School of Psychology and Neuroscience, University of Bristol Jeff Mitchell, School of Engineering and Informatics, University of Sussex Commentary on Futrell, R., & Mahowald, K. (in press). How linguistics learned to stop worrying and love the language models. Abstract According to Futrell and Mahowald (F&M), both infants and language models (LMs) find attested languages easier to learn than "impossible languages" that have unnatural structures. We review the literature and show that LMs often learn attested and many impossible languages equally well. Difficult to learn impossible languages are simply more complex (or random).

We thank the reviewers for their careful consideration of our work. R2 suggested that an analysis on non-toy models would be interesting to see. R3 believed that the synthetic experiment was not suited to the model class. We expect our analysis on smaller models to extrapolate to larger ones (R2). We regret that we were not clearer about how our aim differs from these studies [McMurray et al. (2012), ME would aid downstream learning as we propose or as is observed in humans in lifelong learning settings.

This paper investigates whether the Critical Period (CP) effects in human language acquisition are observed in self-supervised speech models (S3Ms). CP effects refer to greater difficulty in acquiring a second language (L2) with delayed L2 exposure onset, and greater retention of their first language (L1) with delayed L1 exposure offset. While previous work has studied these effects using textual language models, their presence in speech models remains underexplored despite the central role of spoken language in human language acquisition. We train S3Ms with varying L2 training onsets and L1 training offsets on child-directed speech and evaluate their phone discrimination performance. We find that S3Ms do not exhibit clear evidence of either CP effects in terms of phonological acquisition. Notably, models with delayed L2 exposure onset tend to perform better on L2 and delayed L1 exposure offset leads to L1 forgetting.

We examine the language capabilities of language models (LMs) from the critical perspective of human language acquisition. Building on classical language development theories, we propose a three-stage framework to assess the abilities of LMs, ranging from preliminary word understanding to complex grammar and complex logical reasoning. Using this framework, we evaluate the generative capacities of LMs using methods from linguistic research. Results indicate that although recent LMs outperform earlier models in overall performance, their developmental trajectory does not strictly follow the path of human language acquisition. Notably, in generation tasks, LMs are more similar to human performance in areas where information is easier to extract from the corpus, such as average word length, clauses, and auxiliary verbs. Newer LMs did not exhibit significant progress in terms of specific dimensions, such as clauses and auxiliary verbs, where the variation across corpora is relatively limited. Register theory offers a plausible explanation for these observations, suggesting that the linguistic features of the training data have a substantial impact on the models' abilities.