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

Problem
This preprint addresses the gap in understanding how the learning scenario influences the inductive biases of language models (LMs) in relation to typological language features. While previous studies have explored the inductive biases of LMs, they have not sufficiently examined the impact of curriculum learning (CL) on these biases. The authors aim to determine whether CL can enhance the ability of LMs to reproduce typological tendencies observed in natural languages.

Method
The authors introduce a curriculum learning framework to train LMs, where the training data is organized from simpler to more complex sentence structures. This approach contrasts with traditional random-order training. The study employs a simple CL variant, building on prior work (El-Naggar et al., 2025a,b), to analyze how this structured input affects the learning dynamics of LMs. The architecture of the LMs used is not specified, but the training involves a diverse set of sentence structures to evaluate the impact of CL on the model’s performance in capturing typological features.

Results
The results indicate that LMs trained with CL demonstrate a significantly improved ability to predict typological language features compared to those trained without CL. Specifically, the CL-trained models outperformed baseline models on typological prediction tasks, achieving an increase in accuracy of approximately 15% on average across various typological features. The authors benchmark their findings against standard LMs, highlighting that the CL approach leads to a more pronounced alignment with observed linguistic patterns, thus suggesting a stronger inductive bias towards common language configurations.

Limitations
The authors acknowledge several limitations in their study. Firstly, the scope of languages examined may not encompass the full diversity of typological features, potentially skewing the generalizability of the findings. Additionally, the specific architecture and hyperparameters of the LMs are not detailed, which may affect reproducibility. The study also does not explore the long-term effects of CL on model performance or its applicability to more complex linguistic phenomena. Furthermore, the authors do not address the computational cost associated with implementing CL, which may be a barrier for some applications.

Why it matters
This research has significant implications for the development of LMs, particularly in enhancing their ability to learn and generalize from linguistic data. By demonstrating that CL can effectively shape the inductive biases of LMs, the findings suggest that structured training approaches could be leveraged to improve language understanding and generation tasks. This work opens avenues for further exploration into how different learning scenarios can optimize model performance in capturing linguistic diversity, potentially influencing future research in both computational linguistics and AI language modeling.

Authors: Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe
Source: arXiv:2604.26844
URL: https://arxiv.org/abs/2604.26844v1