We introduce quantitative metrics for child language acquisition to evaluate language models. Our focus is on the formal syntactic and functional discourse properties of determiners in English, which young children acquire early and accurately. We propose Contextual Alternative Choice (CAC), a new prompting method which provides targeted tests for both syntactic and discourse knowledge of language. The method enables direct comparison of language models against children, and more importantly, against statistical benchmarks independently established in empirical research. No current model trained on a comparable amount of data simultaneously meet both formal and functional benchmarks like human children, but some very large models do. We present our results as methodological and technical contributions, with specific emphasis on cognitive status of language models.

The success of neural language models (LMs) on many technological tasks has brought about their potential relevance as scientific theories of language despite some clear differences between LM training and child language acquisition. In this paper we argue that some of the most prominent benchmarks for evaluating the syntactic capacities of LMs may not be sufficiently rigorous. In particular, we show that the template-based benchmarks lack the structural diversity commonly found in the theoretical and psychological studies of language. When trained on small-scale data modeling child language acquisition, the LMs can be readily matched by simple baseline models. We advocate for the use of the readily available, carefully curated datasets that have been evaluated for gradient acceptability by large pools of native speakers and are designed to probe the structural basis of grammar specifically. On one such dataset, the LI-Adger dataset, LMs evaluate sentences in a way inconsistent with human language users. We conclude with suggestions for better connecting LMs with the empirical study of child language acquisition.

Any test that promises to assess Human Knowledge of Language (KoL) for any statistically-based Language Model (LM) must meet three requirements: (1) comprehensive coverage of linguistic phenomena; (2) replicable and statistically-vetted human judgement data; and (3) test the LM’s ability to track the gradience of sentence acceptability. To this end, we propose here the LI-Adger dataset: a comprehensive collection of 519 sentence types (4177 sentences) spanning the field of current generative linguistics, accompanied by attested and replicable human acceptability judgements (Sprouse & Almeida, 2012; Sprouse et al. 2013; Sprouse & Almeida, 2017). Finally, we posit the Acceptability Delta Criterion (ADC), an evaluation metric that tests how well a LM can track changes in human acceptability judgements across minimal pairs instead of testing whether the LM assigned a greater likelihood to the expert-labeled acceptable sequence of a minimal pair (S_1 > S_2). We benchmark six different BERT (Devlin et al. 2018) models and a baseline trigram model with the ADC. Although the best performing BERT model scores 94%, and the trigram scores 75% classification accuracy under the traditional metric, performance drops precipitously to 38% for BERT and 30% for the trigram model under the ADC. Adopting the ADC reveals how much harder it is for LMs to track the gradience of acceptability across minimal pairs. With this work, we propose and provide the three necessary requirements for a comprehensive linguistic analysis and test of the apparently Human KoL exhibited by LMs that we believe is currently missing in the field of Computational Linguistics.