When Babies Teach Babies: Can student knowledge sharing outperform Teacher-Guided Distillation on small datasets?

Autor:	Iyer, Srikrishna
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	We present our submission to the BabyLM challenge, aiming to push the boundaries of data-efficient language model pretraining. Our method builds upon deep mutual learning, introducing a student model search for diverse initialization. We address the limitation of treating students equally by formulating weighted mutual learning as a bi-level optimization problem. The inner loop learns compact students through online distillation, while the outer loop optimizes weights for better knowledge distillation from diverse students. This dynamic weighting strategy eliminates the need for a teacher model, reducing computational requirements. Our evaluations show that teacher-less methods can match or surpass teacher-supervised approaches. Comment: Accepted to BabyLM challenge, CoNLL Workshop, EMNLP 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.16487 Zobrazit plný text záznamu View this record from Arxiv