# BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop

arXiv: https://arxiv.org/abs/2502.10645 · HTML: https://mlsanity.com/paper/2502.10645/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://mlsanity.com/paper/2405.14734/) (2024)
- [GIT: A Generative Image-to-text Transformer for Vision and Language](https://mlsanity.com/paper/2205.14100/) (2022)
- [Connecting Vision and Language with Localized Narratives](https://mlsanity.com/paper/1912.03098/) (2019)
- [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)
- [Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations](https://mlsanity.com/paper/2405.13828/) (2024)

## Built on by (most cited first, 24 of 24)

- [Do Construction Distributions Shape Formal Language Learning In German BabyLMs?](https://mlsanity.com/paper/2503.11593/) (2025)
- [Once Upon a Time: Interactive Learning for Storytelling with Small Language Models](https://mlsanity.com/paper/2509.15714/) (2025)
- [Dialogue Is Not Enough to Make a Communicative BabyLM (But Neither Is Developmentally Inspired Reinforcement Learning)](https://mlsanity.com/paper/2510.20358/) (2025)
- [Minimal Pair-Based Evaluation of Code-Switching](https://mlsanity.com/paper/2506.01840/) (2025)
- [On the robustness of modeling grounded word learning through a child's egocentric input](https://mlsanity.com/paper/2507.14749/) (2025)
- [UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions](https://mlsanity.com/paper/2504.20304/) (2025)
- [LongTail-Swap: benchmarking language models' abilities on rare words](https://mlsanity.com/paper/2510.04268/) (2025)
- [Mask and You Shall Receive: Optimizing Masked Language Modeling For Pretraining BabyLMs](https://mlsanity.com/paper/2510.20475/) (2025)
- [Playpen: An Environment for Exploring Learning Through Conversational Interaction](https://mlsanity.com/paper/2504.08590/) (2025)
- [Influence-driven Curriculum Learning for Pre-training on Limited Data](https://mlsanity.com/paper/2508.15475/) (2025)
- [Masked Diffusion Language Models with Frequency-Informed Training](https://mlsanity.com/paper/2509.05056/) (2025)
- [Rethinking the Role of Text Complexity in Language Model Pretraining](https://mlsanity.com/paper/2509.16551/) (2025)
- [What is the Best Sequence Length for BABYLM?](https://mlsanity.com/paper/2510.19493/) (2025)
- [Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models](https://mlsanity.com/paper/2510.01845/) (2025)
- [Contrastive Decoding for Synthetic Data Generation in Low-Resource Language Modeling](https://mlsanity.com/paper/2510.08245/) (2025)
- [A Unified Assessment of the Poverty of the Stimulus Argument for Neural Language Models](https://mlsanity.com/paper/2602.09992/) (2026)
- [Structural priors for data-efficient language learning](https://mlsanity.com/paper/2609.11505/) (2026)
- [BabyLM's First Constructions: Causal probing provides a signal of learning](https://mlsanity.com/paper/2506.02147/) (2025)
- [Beyond Repetition: Text Simplification and Curriculum Learning for Data-Constrained Pretraining](https://mlsanity.com/paper/2509.24356/) (2025)
- [BitMar: Low-Bit Multimodal Fusion with Episodic Memory for Edge Devices](https://mlsanity.com/paper/2510.10560/) (2025)
- [Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?](https://mlsanity.com/paper/2511.08199/) (2025)
- [CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language Models](https://mlsanity.com/paper/2602.21978/) (2026)
- [Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model](https://mlsanity.com/paper/2609.11870/) (2026)
- [A retrospective analysis on the use of LLMs to study infant syntax learning](https://mlsanity.com/paper/2609.26539/) (2026)

## Abstract (arXiv)

BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 3rd BabyLM competition. As in previous years, we call for participants in the data-efficient pretraining challenge in the general track. This year, we also offer a new track: INTERACTION. This new track encourages interactive behavior, learning from a teacher, and adapting the teaching material to the student. We also call for papers outside the competition in any relevant areas. These include training efficiency, cognitively plausible research, weak model evaluation, and more.