8 citations · 20 across the 13 of their papers we have counts for
4 papers · 1 filter
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs
Hao Kang, Srikant Bharadwaj, James Hensman +3
Large language model (LLM) inference demands significant amount of computation and memory, especially in the key attention mechanism. While techniques, such as quantization and acc…
Ensuring Fair LLM Serving Amid Diverse Applications
Redwan Ibne Seraj Khan, Kunal Jain, Haiying Shen +12
In a multi-tenant large language model (LLM) serving platform hosting diverse applications, some users may submit an excessive number of requests, causing the service to become una…
Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective
Lukas Wutschitz, Boris Köpf, Andrew Paverd +6
Modern machine learning systems use models trained on ever-growing corpora. Typically, metadata such as ownership, access control, or licensing information is ignored during traini…
Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa +3
Neural language models are known to have a high capacity for memorization of training samples. This may have serious privacy implications when training models on user content such…