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cs.CL2024
Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters
Euiin Yi, Taehyeon Kim, Hongseok Jeung +2
Large language models (LLMs) have revolutionized natural language processing and broadened their applicability across diverse commercial applications. However, the deployment of th…
cs.CL2024★ 3 cited
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Hoyeon Chang, Jinho Park, Seonghyeon Ye +4
Despite the recent observation that large language models (LLMs) can store substantial factual knowledge, there is a limited understanding of the mechanisms of how they acquire fac…
cs.DC2024
ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference
Hyungjun Oh, Kihong Kim, Jaemin Kim +4
This paper presents ExeGPT, a distributed system designed for constraint-aware LLM inference. ExeGPT finds and runs with an optimal execution schedule to maximize inference through…