2 citations · 2 across the 3 of their papers we have counts for
5 papers
Learning an Anchored Prompt Space for Continual Adaptation of Large Language Models
Rongguang Ye, Zhan Zhuang, Yichen Wu +2
Continually adapting large language models requires acquiring new knowledge while preserving previously learned capabilities. Although jointly adapting model parameters and task-sp…
Curvature-Guided Mixing for MLLM Adaptation
Jinglong Yang, Jiaxuan He, Wenjian Huang +2
Fine-tuning Multimodal Large Language Models (MLLMs) on specialized tasks often leads to catastrophic forgetting of their general capabilities. Existing model merging methods to co…
One-Token Verification for Reasoning Correctness Estimation
Zhan Zhuang, Xiequn Wang, Zebin Chen +4
Recent breakthroughs in large language models (LLMs) have led to notable successes in complex reasoning tasks, such as mathematical problem solving. A common strategy for improving…
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…
Extend Wave Function Collapse to Large-Scale Content Generation
Yuhe Nie, Shaoming Zheng, Zhan Zhuang +1
Wave Function Collapse (WFC) is a widely used tile-based algorithm in procedural content generation, including textures, objects, and scenes. However, the current WFC algorithm and…