6 papers · 1 filter
NeurIPT: Foundation Model for Neural Interfaces
Zitao Fang, Chenxuan Li, Hongting Zhou +7
Electroencephalography (EEG) has wide-ranging applications, from clinical diagnosis to brain-computer interfaces (BCIs). With the increasing volume and variety of EEG data, there h…
Multi-objective Large Language Model Alignment with Hierarchical Experts
Zhuo Li, Guodong Du, Weiyang Guo +8
Aligning large language models (LLMs) to simultaneously satisfy multiple objectives remains a significant challenge, especially given the diverse and often conflicting nature of hu…
Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer
Guodong Du, Zitao Fang, Jing Li +10
Foundation models and their checkpoints have significantly advanced deep learning, boosting performance across various applications. However, fine-tuned models often struggle outsi…
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling
Junlin Li, Guodong DU, Jing Li +8
Fine-tuning Large Language Models (LLMs) with multimodal encoders on modality-specific data expands the modalities that LLMs can handle, leading to the formation of Multimodal LLMs…
Knowledge Fusion of Large Language Models Via Modular SkillPacks
Guodong Du, Zhuo Li, Xuanning Zhou +9
Cross-capability transfer is a key challenge in large language model (LLM) research, with applications in multi-task integration, model compression, and continual learning. Recent…
To See a World in a Spark of Neuron: Disentangling Multi-task Interference for Training-free Model Merging
Zitao Fang, Guodong DU, Shuyang Yu +6
Fine-tuning pre-trained models on targeted datasets enhances task-specific performance but often comes at the expense of generalization. Model merging techniques, which integrate m…