What Kind of Language is Easy to Language-Model Under Curriculum Learning?

2026-04-29 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computational Linguistics · Depth: Expert, quick

Summary

A new study investigates how curriculum learning (CL) interacts with the inductive bias of language models (LMs) to influence the learnability of typologically diverse languages. Researchers expanded on existing LM-based explorations by El-Naggar et al. (2025a,b) with a simplified CL variant. The core finding is that starting LMs with simpler sentences, rather than randomly ordered input, significantly alters their apparent inductive bias. This research explores whether the learning biases of LMs can predict and reproduce common typological patterns observed across thousands of attested languages, ranging from rare configurations like object-verb-subject to common ones such as subject-object-verb word order.

Key takeaway

For research scientists developing or training language models, understanding the interaction between curriculum learning and inductive bias is crucial. Your choice of learning scenario, particularly starting with simpler sentences, can substantially alter the model's inherent biases and its capacity to reproduce linguistic typologies. Consider implementing curriculum learning to potentially improve model performance and alignment with natural language structures.

Key insights

Curriculum learning significantly impacts language models' inductive bias, influencing their ability to learn typological language patterns.

Principles

• Typological tendencies can be predicted by LM learning bias.
• Learning scenario interacts with LM inductive bias.

Method

The study expands existing LM exploration with a simple curriculum learning variant, starting with simpler sentences instead of random input.

In practice

• Use curriculum learning for language model training.
• Prioritize simpler sentences in initial training phases.

Topics

• Language Models
• Curriculum Learning
• Language Typology
• Inductive Bias
• Computational Linguistics

Best for: Research Scientist, AI Scientist, NLP Engineer

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