Filling in the Mechanisms: How do LMs Learn Filler-Gap Dependencies under Developmental Constraints?

2026-04-15 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Research investigated whether large language models (LLMs) trained on developmentally feasible data quantities develop shared representations for filler-gap dependencies, similar to humans. Utilizing Distributed Alignment Search (DAS) on LMs from the BabyLM challenge, the study evaluated representation transfer between wh-questions and topicalization, which differ significantly in input frequency. The findings indicate that shared, item-sensitive mechanisms for these dependencies can emerge even with limited training data. However, the study also highlights that LMs still demand substantially more data than humans to achieve comparable linguistic generalizations, underscoring the necessity for incorporating language-specific biases into computational models of language acquisition.

Key takeaway

For AI scientists developing language acquisition models, these findings suggest that while shared linguistic mechanisms can emerge in LLMs, current models are still highly data-inefficient compared to human learning. You should explore integrating language-specific inductive biases to reduce data requirements and improve generalization, moving beyond purely data-driven approaches for more human-like language acquisition.

Key insights

LLMs show shared, item-sensitive filler-gap dependency mechanisms, but require more data than humans.

Principles

• Shared representations are crucial for filler-gap dependencies.
• Data quantity impacts generalization in language models.

Method

Distributed Alignment Search (DAS) was applied to BabyLM challenge models to evaluate representation transfer of filler-gap dependencies between wh-questions and topicalization.

In practice

• Consider data efficiency in language model training.
• Integrate linguistic biases for human-like acquisition.

Topics

• Filler-Gap Dependencies
• Language Models
• Distributed Alignment Search
• BabyLM Challenge
• Language Acquisition

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.