[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

• URL: http://arxiv.org/abs/2404.06214v2
• Date: Sat, 27 Jul 2024 18:50:28 GMT
• Title: [Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
• Authors: Leshem Choshen, Ryan Cotterell, Michael Y. Hu, Tal Linzen, Aaron Mueller, Candace Ross, Alex Warstadt, Ethan Wilcox, Adina Williams, Chengxu Zhuang,
• Abstract summary: This CfP provides rules for the BabyLM Challenge 2024-2025. The overarching goals of the challenge remain the same. We replace the loose track with a paper track. We relax the rules around pretraining data. We introduce a multimodal vision-and-language track.
• Score: 81.34965784440176
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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