The goal of the BabyLM is to stimulate new research connections between cognitive modeling and language model pretraining. We invite contributions in this vein to the BabyLM Workshop, which will also include the 4th iteration of the BabyLM Challenge. As in previous years, the challenge features two ``standard'' tracks (Strict and Strict-Small), in which participants must train language models on under 100M or 10M words of data, respectively. This year, we move beyond our previous English-only pretraining datasets with a new Multilingual track, focusing on English, Dutch, and Chinese. For the workshop, we call for papers related to the overall theme of BabyLM, which includes training efficiency, small-scale training datasets, cognitive modeling, model evaluation, and architecture innovation.
@article{arxiv.2602.20092,
title = {BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop},
author = {Leshem Choshen and Ryan Cotterell and Mustafa Omer Gul and Jaap Jumelet and Tal Linzen and Aaron Mueller and Suchir Salhan and Raj Sanjay Shah and Alex Warstadt and Ethan Gotlieb Wilcox},
journal= {arXiv preprint arXiv:2602.20092},
year = {2026}
}
Comments
8 pages, 1 table. arXiv admin note: substantial text overlap with arXiv:2502.10645