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Announcing the BabyLM Challenge, the shared task at CoNLL/CMCL 2023!
The goal of this shared task is to encourage researchers with an interest in pretraining and/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 formulating an exciting open problem and establishing a community around it.
Huge effort has been put towards optimizing LM pretraining at massive scales in the last several years. While increasingly larger models often get the most attention, datasets have also grown by orders of magnitude. For example, Chinchilla is exposed to 1.4 trillion words during training—well over 10000 words for every one word a 13-year-old human has encountered in their entire life.
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.
The task has three tracks, two of which restrict the training data to pre-released datasets of 10M and 100M words and are dedicated to explorations of approaches such as architectural variations, self-supervised objectives, and/or curriculum learning. The final track only restricts the amount of text used, allowing innovation in the choice of the data, its domain, and even its modality (i.e., data from sources other than text is welcome). We will release a shared evaluation pipeline which evaluates on a variety of benchmarks and tasks, including targeted syntactic evaluations and natural language understanding.
Important dates:
January 2023: Training data released (see website for download)
March 2023: Evaluation pipeline release
July 15, 2023: Results due
August 1, 2023: Paper submissions due
Date TBA: Presentation at CoNLL
For more information, visit the BabyLM website ( https://babylm.github.io/ ) or consult our extended call for papers ( https://arxiv.org/abs/2301.11796 ).