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Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 20 -
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Paper • 2402.10644 • Published • 81 -
Repeat After Me: Transformers are Better than State Space Models at Copying
Paper • 2402.01032 • Published • 24 -
Zoology: Measuring and Improving Recall in Efficient Language Models
Paper • 2312.04927 • Published • 2
Collections
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Collections including paper arxiv:2310.01889
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Attention Is All You Need
Paper • 1706.03762 • Published • 55 -
ImageNet Large Scale Visual Recognition Challenge
Paper • 1409.0575 • Published • 8 -
Sequence to Sequence Learning with Neural Networks
Paper • 1409.3215 • Published • 3 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 13
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Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
Paper • 2401.03462 • Published • 27 -
MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers
Paper • 2305.07185 • Published • 9 -
YaRN: Efficient Context Window Extension of Large Language Models
Paper • 2309.00071 • Published • 68 -
Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache
Paper • 2401.02669 • Published • 16