5 citations · 7 across the 7 of their papers we have counts for
7 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…
Compositional Generalization in Spoken Language Understanding
Avik Ray, Yilin Shen, Hongxia Jin
State-of-the-art spoken language understanding (SLU) models have shown tremendous success in benchmark SLU datasets, yet they still fail in many practical scenario due to the lack…
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
Jun Yan, Vikas Yadav, Shiyang Li +6
Instruction-tuned Large Language Models (LLMs) have become a ubiquitous platform for open-ended applications due to their ability to modulate responses based on human instructions.…
Instruction-following Evaluation through Verbalizer Manipulation
Shiyang Li, Jun Yan, Hai Wang +4
While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challe…