23 citations · 121 across the 23 of their papers we have counts for
8 papers · 1 filter
FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing
James Seale Smith, Chi-Heng Lin, Shikhar Tuli +5
The rapid proliferation of large language models (LLMs) in natural language processing (NLP) has created a critical need for techniques that enable efficient deployment on memory-c…
DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models
Shangqian Gao, Chi-Heng Lin, Ting Hua +4
Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, including language modeling, understanding, and generation. However, the…
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling
Shikhar Tuli, Chi-Heng Lin, Yen-Chang Hsu +3
Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we…
Numerical Optimizations for Weighted Low-rank Estimation on Language Model
Ting Hua, Yen-Chang Hsu, Felicity Wang +3
Singular value decomposition (SVD) is one of the most popular compression methods that approximate a target matrix with smaller matrices. However, standard SVD treats the parameter…
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding
Ting Hua, Yilin Shen, Changsheng Zhao +2
Domain classification is the fundamental task in natural language understanding (NLU), which often requires fast accommodation to new emerging domains. This constraint makes it imp…
Automatic Mixed-Precision Quantization Search of BERT
Changsheng Zhao, Ting Hua, Yilin Shen +2
Pre-trained language models such as BERT have shown remarkable effectiveness in various natural language processing tasks. However, these models usually contain millions of paramet…
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU
Yilin Shen, Yen-Chang Hsu, Avik Ray +1
Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to d…
An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction
Yu Wang, Yilin Shen, Hongxia Jin
Most of the existing spoken language understanding systems can perform only semantic frame parsing based on a single-round user query. They cannot take users' feedback to update/ad…