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

arXiv:2609.26539 · cs.CL · Submitted 2026-09-22 · Read on arXiv

cs.CL

Submitted: 2026-09-22

Updated: 2026-09-22

Journal ref: EMNLP 2026 Main Conference, ACL SIGDAT, Oct 2026, Budapest (Hungary), Hungary

Code: https://github.com/alexwarstadt/blimphttps:

Project page: https://babylm.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 51× in this paper · explained in 2 episodes
• Generalization — 20× in this paper · explained in 13 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Transformer — 11× in this paper · explained in 2 episodes
• Natural Language Processing — 5× in this paper
• Scaling Laws — 3× in this paper · explained in 4 episodes
• Adaptive Strategy — 2× in this paper · explained in 1 episode
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Parasitic Gaps — 2× in this paper
• Pragmatics — 2× in this paper · explained in 2 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Across the Board — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Lexical Diversity — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Primary Linguistic Data — 1× in this paper

Sources

• BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop
• BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop
• [Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
• TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
• Training Compute-Optimal Large Language Models
• Scaling Laws for Neural Language Models
• What can linguistics and deep learning contribute to each other?
• Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
• On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior
• Deep Learning is Not So Mysterious or Different

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