5 papers
HASTE: Hardware-Aware Dynamic Sparse Training for Large Output Spaces
Nasib Ullah, Jinbin Zhang, Jean Lucien Randrianantenaina +2
Extreme multi-label classification (XMC) involves learning models over large output spaces with millions of labels, making the output layer a memory-compute bottleneck. While spars…
DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models
Jinbin Zhang, Nasib Ullah, Erik Schultheis +1
Speculative decoding accelerates LLM inference by letting a small drafter propose multiple tokens which a large target model verifies once per speculation step. As vocabularies sca…
ELMO: Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces
Jinbin Zhang, Nasib Ullah, Erik Schultheis +1
Large output spaces, also referred to as Extreme multilabel classification (XMC), is a setting that arises, e.g., in large-scale tagging and product-to-product recommendation, and…
Large Language Model as a Teacher for Zero-shot Tagging at Extreme Scales
Jinbin Zhang, Nasib Ullah, Rohit Babbar
Extreme Multi-label Text Classification (XMC) entails selecting the most relevant labels for an instance from a vast label set. Extreme Zero-shot XMC (EZ-XMC) extends this challeng…
Navigating Extremes: Dynamic Sparsity in Large Output Spaces
Nasib Ullah, Erik Schultheis, Mike Lasby +2
In recent years, Dynamic Sparse Training (DST) has emerged as an alternative to post-training pruning for generating efficient models. In principle, DST allows for a more memory ef…