BabyLM Challenge
Sample-efficient pretraining on a developmentally plausible corpus
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.
Submission Tracks
We will evaluate models in three cateogries: strict , strict-small and loose .
• Strict and Strict-Small: The strict and strict-small tracks require that submissions are trained exclusively on a fixed dataset. The only difference between these tracks is the size of the dataset (~10M words vs. ~100M words). Winners will be determined based on performance on a shared evaluation set consisting of syntactic evaluation and NLU tasks.
• Loose: Submissions are still limited to ~100M words or less of training data, and will be tested on the shared evaluation set. However, they are permitted to use unlimited non-linguistic data. Additionally, training on additional text is allowed without limits if that text is generated by a model trained following the above restrictions. Winners will be selected holistically based on evaluation performance, relevance to the shared task goals, potential impact, and originality.
Pretraining Data
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:
• Under 100M words: Children are exposed to 2M-7M words per year (Gilkerson et al., 2017) . Choosing the beginning of adolescence (age 13) as a cutoff, children are exposed to at maximum 91M words, which we round up to 100M.
• Mostly transcribed speech: Most of the input to children is spoken; thus, our dataset focuses on transcribed speech.
• Mixed domain, consisting of the following sources: CHILDES (child-directed speech), Subtitles (speech), BNC (speech), TED talks (speech), children's books (simple written language).
See the call for papers for a detailed breakdown of the pretraining datasets.
Evaluation Pipeline
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.
Results Submissions
The deadline for results submissions is July 15 July 22, 23:59 anywhere on earth (UTC-12).
Submissions must be made through Dynabench.
Paper Submissions
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:
Archival full papers (up to 8 pages)
Non-archival extended abstract (up to 2 pages)
Submissions of both types are:
required to use the EMNLP 2023 template,
given unlimited space for references,
given unlimited space for appendices,
given extra space for ethics/limitations, though these sections are optional
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.