Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty

• URL: http://arxiv.org/abs/2308.02019v2
• Date: Tue, 24 Oct 2023 17:58:42 GMT
• Title: Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty
• Authors: Inar Timiryasov and Jean-Loup Tastet
• Abstract summary: We trained an ensemble consisting of a GPT-2 and small LLaMA models on a developmentally-plausible, 10M-word BabyLM dataset. We distilled it into a small, 58M- parameter LLaMA model, which exceeds in performance both of its teachers as well as a similar model trained without distillation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present our submission to the BabyLM challenge, whose goal was to improve the sample efficiency of language models. We trained an ensemble consisting of a GPT-2 and small LLaMA models on the developmentally-plausible, 10M-word BabyLM dataset, then distilled it into a small, 58M-parameter LLaMA model, which exceeds in performance both of its teachers as well as a similar model trained without distillation. This suggests that distillation can not only retain the full performance of the teacher model when the latter is trained on a sufficiently small dataset; it can exceed it, and lead to significantly better performance than direct training.

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