Language Acquisition Device in Large Language Models

2026-05-19 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

Researchers from The University of Tokyo propose "LAD-inspired PPT," a pre-pretraining (PPT) framework for Large Language Models (LLMs) that uses MP-Struct, a formal language inspired by the Language Acquisition Device (LAD) hypothesis and Minimalist Program. This method aims to inject natural-language-like structural biases, improving data efficiency compared to training from scratch. A brief 500-step PPT with MP-Struct on Pythia-1B models achieved a 29% average efficiency gain, comparable to the strong $k$-Shuffle Dyck baseline. MP-Struct also imparted a human-like resistance to structurally implausible languages (e.g., Reverse sequences) and showed lower reliance on lexical co-occurrence. Analysis revealed that "functional landmarks" in MP-Struct Core, which reduce dependency resolution ambiguity, are a key driver of efficiency, challenging the prior "expressivity hypothesis" that effective PPT languages must be C-RASP definable.

Key takeaway

For research scientists developing more data-efficient LLMs, consider integrating LAD-inspired pre-pretraining with MP-Struct. This approach, which leverages linguistically motivated inductive biases and functional landmarks, can significantly improve learning efficiency and instill more human-like structural processing, potentially reducing the massive data requirements of current models. Focus on designing synthetic languages that not only possess hierarchical expressivity but also offer clear structural cues to minimize dependency identification ambiguity.

Key insights

LAD-inspired pre-pretraining with MP-Struct improves LLM data efficiency and instills human-like linguistic biases.

Principles

• Innate constraints restrict hypothesis space.
• Functional landmarks reduce dependency ambiguity.
• Effective PPT needs structural accessibility.

Method

LAD-inspired PPT involves pre-pretraining LLMs on MP-Struct, a synthetic language encoding hierarchical composition, feature-based dependencies, and long-distance displacement via Merge, Agree, and Move operations, before standard natural language pretraining.

In practice

• Pre-pretrain LLMs with MP-Struct for 29% efficiency gains.
• Use functional landmarks to reduce dependency ambiguity.
• Design synthetic languages with explicit structural cues.

Topics

• Language Acquisition Device
• Pre-pretraining
• MP-Struct
• Large Language Models
• Inductive Biases

Code references

• osekilab/LAD-PPT
• jkallini/mission-impossible-language-models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.