Measuring Form and Function in Language Models

Problem
This preprint addresses the gap in evaluating language models (LMs) against empirical benchmarks of child language acquisition, specifically focusing on the syntactic and functional properties of determiners in English. Existing evaluations often lack a direct comparison to human language development, particularly in the early stages where children demonstrate robust understanding of these properties. The authors aim to establish a framework that quantitatively measures LMs’ capabilities in a manner analogous to child language acquisition, thereby providing a more nuanced understanding of their cognitive status.

Method
The authors introduce a novel prompting method called Contextual Alternative Choice (CAC), designed to assess both syntactic and discourse knowledge in LMs. CAC involves presenting models with contextually rich prompts that require them to choose between alternatives, thereby testing their understanding of determiners in various syntactic and functional contexts. The evaluation framework is grounded in empirical benchmarks derived from child language acquisition studies, allowing for a direct comparison between LMs and human performance. The paper does not disclose specific training compute or data sizes for the models evaluated, but it emphasizes that the models are assessed against statistical benchmarks established in prior empirical research.

Results
The results indicate that while no current language model trained on a comparable dataset meets both formal and functional benchmarks simultaneously as human children do, some very large models exhibit significant capabilities. For instance, the authors report that certain state-of-the-art models achieve performance levels that are competitive with children in specific syntactic tasks, although they fall short in functional discourse tasks. The paper provides quantitative metrics that highlight these discrepancies, although specific numerical results and effect sizes are not detailed in the summary.

Limitations
The authors acknowledge several limitations, including the potential biases in the selected benchmarks and the inherent differences in the learning processes of LMs versus children. They also note that the CAC method may not capture the full complexity of child language acquisition, as it focuses on a narrow aspect of language (determiners). Additionally, the reliance on large models raises questions about the generalizability of the findings to smaller or less complex models. The paper does not address the implications of model interpretability or the potential for overfitting to the benchmarks used.

Why it matters
This work has significant implications for the field of natural language processing (NLP) and cognitive science. By establishing a framework for evaluating LMs against child language acquisition benchmarks, it opens avenues for more rigorous assessments of model capabilities and limitations. This could lead to improved model architectures that better mimic human language learning processes. Furthermore, the findings challenge the notion that larger models inherently possess superior language understanding, prompting a reevaluation of how we measure and interpret language proficiency in artificial systems. The insights gained from this research could inform future developments in LMs, particularly in enhancing their syntactic and functional understanding.

Authors: Héctor Javier Vázquez Martínez, Charles Yang
Source: arXiv:2605.28616
URL: https://arxiv.org/abs/2605.28616v1