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Attention Is All You Need
Paper • 1706.03762 • Published • 55 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 17 -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 14 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 13
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Collections including paper arxiv:2307.09288
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 17 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 7 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 13 -
OPT: Open Pre-trained Transformer Language Models
Paper • 2205.01068 • Published • 2
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Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 40 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 17 -
Attention Is All You Need
Paper • 1706.03762 • Published • 55 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 244
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Nemotron-4 15B Technical Report
Paper • 2402.16819 • Published • 45 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 55 -
RWKV: Reinventing RNNs for the Transformer Era
Paper • 2305.13048 • Published • 17 -
Reformer: The Efficient Transformer
Paper • 2001.04451 • Published