Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies

Suchir Salhan, Richard Diehl Martinez, Zébulon Goriely, Paula Buttery
10/30/2024
cs.CLcs.AI

No official code found. Best implementation: suchirsalhan/MAO-CLIMB (0 stars, last commit 2024-10-28).

Abstract

Curriculum Learning has been a popular strategy to improve the cognitive plausibility of Small-Scale Language Models (SSLMs) in the BabyLM Challenge. However, it has not led to considerable improvements over non-curriculum models. We assess whether theoretical linguistic acquisition theories can be used to specify more fine-grained curriculum learning strategies, creating age-ordered corpora of Child-Directed Speech for four typologically distant language families to implement SSLMs and acquisition-inspired curricula cross-lingually. Comparing the success of three objective curricula (Growing, Inwards and MMM) that precisely replicate the predictions of acquisition theories on a standard SSLM architecture, we find fine-grained acquisition-inspired curricula can outperform non-curriculum baselines and performance benefits of curricula strategies in SSLMs can be derived by specifying fine-grained language-specific curricula that precisely replicate language acquisition theories.

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Multilingual BabyLMs for the 2nd BabyLM Challenge

01Shell, PythonApr 10, 20241 years ago

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Cite this paper

@article{salhan2024less,
  title  = {Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies},
  author = {Suchir Salhan and Richard Diehl Martinez and Zébulon Goriely and Paula Buttery},
  year   = {2024},
  eprint = {2410.22886},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.22886v2}
}

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