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Found 3 papers, 1 papers with code
Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models
Large language models (LLMs) with billions of parameters and pretrained on massive amounts of data are now capable of near or better than state-of-the-art performance in a variety of downstream natural language processing tasks.
Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression
Compactness in deep learning can be critical to a model's viability in low-resource applications, and a common approach to extreme model compression is quantization.
Improving Autoregressive NLP Tasks via Modular Linearized Attention
Various natural language processing (NLP) tasks necessitate models that are efficient and small based on their ultimate application at the edge or in other resource-constrained environments.
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