5 papers
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…
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…
Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?
Nasib Ullah, Erik Schultheis, Jinbin Zhang +1
Extreme multilabel classification (XMLC) problems occur in settings such as related product recommendation, large-scale document tagging, or ad prediction, and are characterized by…
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…
Multi-task Learning for Chinese Word Usage Errors Detection
Jinbin Zhang, Heng Wang
Chinese word usage errors often occur in non-native Chinese learners' writing. It is very helpful for non-native Chinese learners to detect them automatically when learning writing…