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

arXiv: https://arxiv.org/abs/2602.20092 · HTML: https://mlsanity.com/paper/2602.20092/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://mlsanity.com/paper/2405.14734/) (2024)
- [Connecting Vision and Language with Localized Narratives](https://mlsanity.com/paper/1912.03098/) (2019)
- [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://mlsanity.com/paper/2504.08165/) (2025)
- [A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics](https://mlsanity.com/paper/1812.08092/) (2018)
- [GPT or BERT: why not both?](https://mlsanity.com/paper/2410.24159/) (2024)
- [[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus](https://mlsanity.com/paper/2404.06214/) (2024)
- [A Bit of a Problem: Measurement Disparities in Dataset Sizes Across Languages](https://mlsanity.com/paper/2403.00686/) (2024)
- [Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interaction](https://mlsanity.com/paper/2510.20411/) (2025)

## Built on by (most cited first, 13 of 13)

- [Measuring Form and Function in Language Models](https://mlsanity.com/paper/2605.28616/) (2026)
- [Structural priors for data-efficient language learning](https://mlsanity.com/paper/2609.11505/) (2026)
- [Subword Segmental BabyLMs: Learning to Tokenise for Sample-Efficient Pretraining](https://mlsanity.com/paper/2609.01151/) (2026)
- [Disentangling Statistical Preemption from Entrenchment in Language Models' Avoidance of Overgeneralization](https://mlsanity.com/paper/2609.01794/) (2026)
- [Data-Efficient Language Modeling: From Frontier Advancement to Principle-Guided Model Improvement](https://mlsanity.com/paper/2609.10702/) (2026)
- [Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model](https://mlsanity.com/paper/2609.11870/) (2026)
- [URCHIN: A Horizontal Spiking Language Model for Data-Constrained Pretraining](https://mlsanity.com/paper/2609.13899/) (2026)
- [Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM](https://mlsanity.com/paper/2609.17435/) (2026)
- [Relational Attention for Data-Efficient Language Modeling](https://mlsanity.com/paper/2609.20530/) (2026)
- [A retrospective analysis on the use of LLMs to study infant syntax learning](https://mlsanity.com/paper/2609.26539/) (2026)
- [Feeding BabyLMs Macaroni: Code-Switching Curricula Cause Cross-Lingual Convergence](https://mlsanity.com/paper/2609.30535/) (2026)
- [Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training](https://mlsanity.com/paper/2610.06161/) (2026)
- [Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training](https://mlsanity.com/paper/2610.20102/) (2026)

## Abstract (arXiv)

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.