BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop

TL;DR

The BabyLM workshop and challenge promote research on small-scale, multilingual language models, emphasizing cognitive modeling, training efficiency, and innovative architectures across English, Dutch, and Chinese.

Contribution

This call for papers introduces a new multilingual track and expands the scope of the BabyLM Challenge to include multiple languages and broader research themes.

Findings

Introduction of a multilingual track with English, Dutch, and Chinese

Focus on training efficiency and small datasets for language models

Encouragement of research linking cognitive modeling and language pretraining

Abstract

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.