Feb. 28, 2024, 5:44 a.m. | Jiachen Zhao, Wenlong Zhao, Andrew Drozdov, Benjamin Rozonoyer, Md Arafat Sultan, Jay-Yoon Lee, Mohit Iyyer, Andrew McCallum

cs.LG updates on arXiv.org arxiv.org

arXiv:2311.08640v3 Announce Type: replace-cross
Abstract: We study semi-supervised sequence generation tasks, where the few labeled examples are too scarce to finetune a model, and meanwhile, few-shot prompted large language models (LLMs) exhibit room for improvement. In this paper, we present the discovery that a student model distilled from a few-shot prompted LLM can commonly generalize better than its teacher to unseen examples on such tasks. We find that the student is able to learn a general pattern from the high-quality …

abstract arxiv collaborative cs.cl cs.lg discovery distillation examples few-shot improvement knowledge language language model language models large language large language model large language models llms paper room semi-supervised study tasks type

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