1 citations · 1 across the 2 of their papers we have counts for
4 papers
Dynamic Noise Preference Optimization: Self-Improvement of Large Language Models with Self-Synthetic Data
Haoyan Yang, Khiem Le, Ting Hua +7
Although LLMs have achieved significant success, their reliance on large volumes of human-annotated data has limited their potential for further scaling. In this situation, utilizi…
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…
ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning
Shangqian Gao, Ting Hua, Reza Shirkavand +10
Large Language Models (LLMs) have demonstrated remarkable abilities in tackling a wide range of complex tasks. However, their huge computational and memory costs raise significant…
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…