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

• URL: http://arxiv.org/abs/2602.20092v2
• Date: Tue, 24 Feb 2026 17:51:23 GMT
• Title: BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop
• Authors: Leshem Choshen, Ryan Cotterell, Mustafa Omer Gul, Jaap Jumelet, Tal Linzen, Aaron Mueller, Suchir Salhan, Raj Sanjay Shah, Alex Warstadt, Ethan Gotlieb Wilcox,
• Abstract summary: The goal of the BabyLM is to stimulate new research connections between cognitive modeling and language model pretraining.<n>This year, we move beyond our previous English-only pretraining datasets with a new track, focusing on English, Dutch, and Chinese.<n>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.
• Score: 73.0356575273869
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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