[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining
  on a developmentally plausible corpus
Leshem Choshen Ryan Cotterell Michael Y. Hu Tal Linzen Aaron Mueller Candace Ross Alex Warstadt Ethan Wilcox Adina Williams Chengxu Zhuang
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.