@inproceedings{choshen-etal-2026-findings,
title = "Findings of the Fourth {B}aby{LM} Challenge {\&} Workshop: Going Beyond {E}nglish",
author = "Choshen, Leshem and
Cotterell, Ryan and
Gul, Mustafa Omer and
Jumelet, Jaap and
Linzen, Tal and
Mueller, Aaron and
Salhan, Suchir and
Shah, Raj Sanjay and
Warstadt, Alex and
Wilcox, Ethan Gotlieb",
editor = "Choshen, Leshem and
Cotterell, Ryan and
Gul, Mustafa Omer and
Jumelet, Jaap and
Linzen, Tal and
Mueller, Aaron and
Salhan, Suchir and
Shah, Raj Sanjay and
Warstadt, Alex and
Wilcox, Ethan Gotlieb",
booktitle = "Proceedings of the Second {B}aby{LM} Workshop",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.babylm-main.37/",
pages = "479--492",
ISBN = "979-8-89176-475-0",
abstract = "This report summarizes the findings from the 4th BabyLM Challenge. The BabyLM Challenge is a shared task aimed at closing the data-efficiency gap between human and machine language learners. This year, the challenge was held as part of the 2nd BabyLM Workshop. We kept the text-only (strict and strict-small) tracks from previous years; interaction-based systems (which previously had a separate track) were folded into these existing tracks. We also introduced a new multilingual track, where language models were required to perform well on BabyLM evaluations in English, Dutch, and Mandarin Chinese. We observe that GPT-BERT remains a strong architectural foundation for sample-efficient pre-training, and that more training epochs, recursion/looping, and data preprocessing are associated with stronger performance in general. The winning submissions to the new multilingual track additionally demonstrate that LLM-generated parallel data can be quite effective at improving multilingual capabilities in low-data settings."
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%0 Conference Proceedings
%T Findings of the Fourth BabyLM Challenge & Workshop: Going Beyond English
%A Choshen, Leshem
%A Cotterell, Ryan
%A Gul, Mustafa Omer
%A Jumelet, Jaap
%A Linzen, Tal
%A Mueller, Aaron
%A Salhan, Suchir
%A Shah, Raj Sanjay
%A Warstadt, Alex
%A Wilcox, Ethan Gotlieb
%Y Choshen, Leshem
%Y Cotterell, Ryan
%Y Gul, Mustafa Omer
%Y Jumelet, Jaap
%Y Linzen, Tal
%Y Mueller, Aaron
%Y Salhan, Suchir
%Y Shah, Raj Sanjay
%Y Warstadt, Alex
%Y Wilcox, Ethan Gotlieb
%S Proceedings of the Second BabyLM Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-475-0
%F choshen-etal-2026-findings
%X This report summarizes the findings from the 4th BabyLM Challenge. The BabyLM Challenge is a shared task aimed at closing the data-efficiency gap between human and machine language learners. This year, the challenge was held as part of the 2nd BabyLM Workshop. We kept the text-only (strict and strict-small) tracks from previous years; interaction-based systems (which previously had a separate track) were folded into these existing tracks. We also introduced a new multilingual track, where language models were required to perform well on BabyLM evaluations in English, Dutch, and Mandarin Chinese. We observe that GPT-BERT remains a strong architectural foundation for sample-efficient pre-training, and that more training epochs, recursion/looping, and data preprocessing are associated with stronger performance in general. The winning submissions to the new multilingual track additionally demonstrate that LLM-generated parallel data can be quite effective at improving multilingual capabilities in low-data settings.
%U https://aclanthology.org/2026.babylm-main.37/
%P 479-492
Markdown (Informal)
[Findings of the Fourth BabyLM Challenge & Workshop: Going Beyond English](https://aclanthology.org/2026.babylm-main.37/) (Choshen et al., BabyLM 2026)
ACL
- Leshem Choshen, Ryan Cotterell, Mustafa Omer Gul, Jaap Jumelet, Tal Linzen, Aaron Mueller, Suchir Salhan, Raj Sanjay Shah, Alex Warstadt, and Ethan Gotlieb Wilcox. 2026. Findings of the Fourth BabyLM Challenge & Workshop: Going Beyond English. In Proceedings of the Second BabyLM Workshop, pages 479–492, Budapest, Hungary. Association for Computational Linguistics.