mlsanityData to 7 Oct 2026

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

arXiv 2602.20092 ↗First posted 23 Feb 2026cs.CLcited by 10

Not on the map. Why?

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 23 Feb 2026.

Builds on

9 of 24 references.

References · 9

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2024SimPO: Simple Preference Optimization with a Reference-Free Rewardcited by 951
  3. 2019Connecting Vision and Language with Localized Narrativescited by 225
  4. 2025Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corporacited by 86
  5. 2018A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguisticscited by 64
  6. 2024[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpuscited by 26
  7. 2024GPT or BERT: why not both?cited by 26
  8. 2024A Bit of a Problem: Measurement Disparities in Dataset Sizes Across Languagescited by 22
  9. 2025Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interactioncited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 10.

10 papers.

  1. 2026Measuring Form and Function in Language Modelscited by 1
  2. 2026Structural priors for data-efficient language learningcited by 1
  3. 2026Subword Segmental BabyLMs: Learning to Tokenise for Sample-Efficient Pretrainingcited by 0
  4. 2026Disentangling Statistical Preemption from Entrenchment in Language Models' Avoidance of Overgeneralizationcited by 0
  5. 2026Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Modelcited by 0
  6. 2026URCHIN: A Horizontal Spiking Language Model for Data-Constrained Pretrainingcited by 0
  7. 2026Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLMcited by 0
  8. 2026Relational Attention for Data-Efficient Language Modelingcited by 0
  9. 2026A retrospective analysis on the use of LLMs to study infant syntax learningcited by 0
  10. 2026Feeding BabyLMs Macaroni: Code-Switching Curricula Cause Cross-Lingual Convergencecited by 0