Huge effort has been put into optimizing LM pretraining at massive scales in the last several years. While growing parameter counts often get the most attention, datasets have also grown by orders of magnitude. For example, Chinchilla sees 1.4 trillion words during training---well over 10000 words for every one word a 13 year old child has heard in their entire life.

The goal of this shared task is to incentivize researchers with an interest in pretraining or cognitive modeling to focus their efforts on optimizing pretraining given data limitations inspired by human development.
Additionally, we hope to democratize research on pretraining—which is typically thought to be practical only for large industry groups—by drawing attention to open problems that can be addressed on a university budget.

Focusing on scaled down pretraining has several potential benefits:
First, small scale pretraining can be a sandbox for developing novel techniques for improving data efficiency. These techniques have the potential to then scale up to larger scales commonly seen in applied NLP or used to enhance current approaches to modeling low-resource languages.
Second, improving our ability to train LMs on the same kinds and quantities of data that humans learn from hopefully will give us greater access to plausible cognitive models of humans and help us understand what allows humans to acquire language so efficiently.

Submissions should be implimented in Huggingface's Transformers library and can be submitted along one of three submission tracks. The data has been released and can be downloaded here.

We will evaluate models in three cateogries: strict , strict-small and loose .

We distribute a developmentally plausible pretraining dataset inspired by the input to children. Submissions must use only this training data to be considered for the strict and strict-small tracks, but may use alternate or additional data for other tracks. The dataset has the following properties:

See the call for papers for a detailed breakdown of the pretraining datasets.

Models will be evaluated on a shared pipeline, hosted at this GitHub link.

The public validation data we use is a mixture of BLiMP and (Super)GLUE tasks. We will hold out additional tasks for our final evaluation of submitted models.

The deadline for results submissions is July 15 July 22, 23:59 anywhere on earth (UTC-12).

Submissions must be made through Dynabench.

The deadline for paper submissions is August 1 August 2, 23:59 anywhere on earth (UTC-12).

Submissions must be made through our OpenReview portal. We accept two types of paper submissions:

Submissions of both types are:

We allow dual submissions of archival papers with one of the co-sponsored workshops (CoNLL and CMCL). In the event that an archival paper is accepted to both BabyLM and one of these workshops, it can only appear in one of their proceedings (i.e., it must be withdrawn from one venue).

BabyLM will hold its own review process, separate from CoNLL and CMCL, and the proceedings will appear in their own volume. The acceptance criteria are based on soundness: We plan only to reject submissions that make incorrect or unjustified claims. Other feedback will be directed at the improvement of submissions.