3 citations · 3 across the 5 of their papers we have counts for
6 papers
Adaptive Utilization of Low-Rank Adaptation via Conditioned Gating
Guang Yang, Changhao Guan, Chao Huang +2
Low-Rank Adaptation (LoRA) achieves parameter-efficient fine-tuning by constraining model updates to a low-rank subspace and has been widely used in practice. However, LoRA typical…
Disentangled Contrastive Learning for Zero-Shot Multilingual Dense Retrieval
Chao Huang, Yufeng Chen, Changhao Guan +3
Multilingual dense retrieval aims to handle queries and documents across different languages based on a unified retriever model. The challenge lies in enabling robust retrieval tra…
Boosting Data Utilization for Multilingual Dense Retrieval
Chao Huang, Fengran Mo, Yufeng Chen +5
Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of di…
Multilingual Collaborative Defense for Large Language Models
Hongliang Li, Jinan Xu, Gengping Cui +3
The robustness and security of large language models (LLMs) has become a prominent research area. One notable vulnerability is the ability to bypass LLM safeguards by translating h…
DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution
Yulong Mao, Kaiyu Huang, Changhao Guan +3
Fine-tuning large-scale pre-trained models is inherently a resource-intensive task. While it can enhance the capabilities of the model, it also incurs substantial computational cos…
Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation
Ning Cheng, Changhao Guan, Jing Gao +7
Touch holds a pivotal position in enhancing the perceptual and interactive capabilities of both humans and robots. Despite its significance, current tactile research mainly focuses…