4 papers
Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures
Yicheng Zhang, Zhen Qin, Zhaomin Wu +2
Large language models (LLMs) are increasingly powering web-based applications, whose effectiveness relies on fine-tuning with large-scale instruction data. However, such data often…
Federated Data-Efficient Instruction Tuning for Large Language Models
Zhen Qin, Zhaomin Wu, Bingsheng He +1
Instruction tuning is a crucial step in improving the responsiveness of pretrained large language models (LLMs) to human instructions. Federated learning (FL) helps to exploit the…
ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
Yunkun Wang, Yue Zhang, Zhen Qin +5
Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhausti…
SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples
Yin Wang, Zixuan Wang, Hao Lu +7
Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. However, the class-imbalanced data distribution in real-world scenarios poses great…