3 papers
cs.CL2025
SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models
Jun Rao, Yunjie Liao, Xuebo Liu +6
Existing alignment methods for preference optimization of large language models (LLMs) aim to enhance model performance by utilizing pairs of positive and negative samples. However…
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.CL2024
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions
Jun Rao, Xuebo Liu, Lian Lian +3
With instruction tuning, Large Language Models (LLMs) can enhance their ability to adhere to commands. Diverging from most works focusing on data mixing, our study concentrates on…