A retrospective analysis on the use of LLMs to study infant syntax learning

2026-09-22

AI Summary

本文回顾分析了使用大型语言模型研究婴儿语法学习的方法，评估了BabyLM挑战中的数据集构建、模型训练和评估方法，并指出了其理论局限性和与婴儿学习者的计算差异。

Abstract

Large language models (LLMs) have increasingly been used to investigate how children acquire syntax at an early stage of development. This is notably the central scientific goal of the BabyLM challenge, a community-wide effort to develop models that achieve human-level syntactic performance while being trained on developmentally realistic corpora. In this paper, we reflect on the use of LLMs in the study of infant syntax learning by providing an epistemological assessment of several studies from this research program. We discuss how datasets are built, which models are implemented, how they are trained and syntactically evaluated. We observe significant assumptions in the methodology of BabyLM and related studies, thus mitigating their theoretical scope. We additionally observe that using developmentally-realistic corpora have limited effects on models performance on commonly-used benchmarks, which suggest important computational differences between LLMs and the infant syntax learner.

Problem

• Large language models
• syntax learning
• infant development
• BabyLM challenge
• epistemological assessment

Innovation

• Large language models
• infant syntax learning
• developmentally realistic corpora