76 citations · 93 across the 6 of their papers we have counts for
6 papers
Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation
Zhan Zhuang, Xiequn Wang, Wei Li +9
Low-rank adaptation (LoRA) has emerged as a leading parameter-efficient fine-tuning technique for adapting large foundation models, yet it often locks adapters into suboptimal mini…
DynamicMind: A Tri-Mode Thinking System for Large Language Models
Wei Li, Yanbin Wei, Qiushi Huang +4
Modern large language models (LLMs) often struggle to dynamically adapt their reasoning depth to varying task complexities, leading to suboptimal performance or inefficient resourc…
Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models
Shuai Fu, Xiequn Wang, Qiushi Huang +1
With the prevalence of large-scale pretrained vision-language models (VLMs), such as CLIP, soft-prompt tuning has become a popular method for adapting these models to various downs…
Learning Retrieval Augmentation for Personalized Dialogue Generation
Qiushi Huang, Shuai Fu, Xubo Liu +4
Personalized dialogue generation, focusing on generating highly tailored responses by leveraging persona profiles and dialogue context, has gained significant attention in conversa…
Selective Prompting Tuning for Personalized Conversations with LLMs
Qiushi Huang, Xubo Liu, Tom Ko +4
In conversational AI, personalizing dialogues with persona profiles and contextual understanding is essential. Despite large language models' (LLMs) improved response coherence, ef…
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
Yanbin Wei, Qiushi Huang, James T. Kwok +1
Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC. They can b…