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- [2005. 14165] Language Models are Few-Shot Learners - arXiv. org
Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches
- Language models are few-shot learners | Proceedings of the 34th . . .
We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning approaches
- Language Models are Few-Shot Learners - NeurIPS
Abstract We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning approaches
- Language Models are Few-Shot Learners - Semantic Scholar
This study presents a meta-learning-driven low-rank adapter pooling method, called AMAL, for leveraging pre-trained language models even with just a few data points and shows that AMAL significantly outperforms previous few-shot learning methods and achieves a new state-of-the-art
- Paper page - Language Models are Few-Shot Learners
Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches
- dblp: Language Models are Few-Shot Learners.
dblp: Language Models are Few-Shot Learners For some months now, the dblp team has been receiving an exceptionally high number of support and error correction requests from the community
- NeurIPS 2020 Language Models are Few-Shot Learners Oral
Tom B Brown · Benjamin Mann · Nick Ryder · Melanie Subbiah · Jared Kaplan · Prafulla Dhariwal · Arvind Neelakantan · Pranav Shyam · Girish Sastry · Amanda Askell · Sandhini Agarwal · Ariel Herbert-Voss · Gretchen M Krueger · Tom Henighan · Rewon Child · Aditya Ramesh · Daniel Ziegler · Jeffrey Wu · Clemens Winter · Chris
- Language Models are Few-Shot Learners - arXiv. org
Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches
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