3 papers
cs.CL2025
AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs
Xiaopeng Ke, Hexuan Deng, Xuebo Liu +4
Despite the impressive performance of large language models (LLMs) in general domains, they often underperform in specialized domains. Existing approaches typically rely on data sy…
cs.CL2025
APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training
Jun Rao, Zepeng Lin, Xuebo Liu +6
Large Language Models (LLMs) often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. Maintaining a balance between do…
cs.LG2024
Privacy-Preserving Federated Learning via Dataset Distillation
ShiMao Xu, Xiaopeng Ke, Xing Su +4
Federated Learning (FL) allows users to share knowledge instead of raw data to train a model with high accuracy. Unfortunately, during the training, users lose control over the kno…