4 citations · 6 across the 12 of their papers we have counts for
4 papers · 1 filter
Cartridges: Lightweight and general-purpose long context representations via self-study
Sabri Eyuboglu, Ryan Ehrlich, Simran Arora +8
Large language models are often used to answer queries grounded in large text corpora (e.g. codebases, legal documents, or chat histories) by placing the entire corpus in the conte…
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…
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…
Zoology: Measuring and Improving Recall in Efficient Language Models
Simran Arora, Sabri Eyuboglu, Aman Timalsina +5
Attention-free language models that combine gating and convolutions are growing in popularity due to their efficiency and increasingly competitive performance. To better understand…