4 citations · 6 across the 12 of their papers we have counts for
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cs.CL2024★ 1 cited
Just read twice: closing the recall gap for recurrent language models
Simran Arora, Aman Timalsina, Aaryan Singhal +6
Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use…
cs.CL2024★ 4 cited
Simple linear attention language models balance the recall-throughput tradeoff
Simran Arora, Sabri Eyuboglu, Michael Zhang +6
Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of atten…