4 citations · 6 across the 8 of their papers we have counts for
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cs.CL2024★ 1 cited
LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference
Qichen Fu, Minsik Cho, Thomas Merth +3
The inference of transformer-based large language models consists of two sequential stages: 1) a prefilling stage to compute the KV cache of prompts and generate the first token, a…
cs.AI2024★ 4 cited
Apple Intelligence Foundation Language Models
Tom Gunter, Zirui Wang, Chong Wang +152
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large serv…
cs.CL2024★ 1 cited
Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation
Thomas Merth, Qichen Fu, Mohammad Rastegari +1
Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts. Their inference cost scales quadratically wit…