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cs.CL2022
OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal +16
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computatio…
cs.CL2022★ 1 cited
Improving In-Context Few-Shot Learning via Self-Supervised Training
Mingda Chen, Jingfei Du, Ramakanth Pasunuru +4
Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot le…