[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
<|reference_start|>[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus: After last year's successful BabyLM Challenge, the competition will be hosted again in 2024/2025. The overarching goals of the challenge remain the same; however, some of the competition rules will be different. The big changes for this year's competition are as follows: First, we replace the loose track with a paper track, which allows (for example) non-model-based submissions, novel cognitively-inspired benchmarks, or analysis techniques. Second, we are relaxing the rules around pretraining data, and will now allow participants to construct their own datasets provided they stay within the 100M-word or 10M-word budget. Third, we introduce a multimodal vision-and-language track, and will release a corpus of 50% text-only and 50% image-text multimodal data as a starting point for LM model training. The purpose of this CfP is to provide rules for this year's challenge, explain these rule changes and their rationale in greater detail, give a timeline of this year's competition, and provide answers to frequently asked questions from last year's challenge.<|reference_end|>
@article{choshen2024[call,
    title={[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining
  on a developmentally plausible corpus},
    author={Leshem Choshen, Ryan Cotterell, Michael Y. Hu, Tal Linzen, Aaron
  Mueller, Candace Ross, Alex Warstadt, Ethan Wilcox, Adina Williams, Chengxu
  Zhuang},
    journal={arXiv preprint arXiv:2404.06214},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06214},
    primaryClass={cs.CL}
}