Fei Tan

ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models

Yilin Jiang, Xiaorong Zhu, Fei Tan, et al.

HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents

Shuqi Cao, Jingyi He, Fei Tan

VEPO: Variable Entropy Policy Optimization for Low-Resource Language Foundation Models

Chonghan Liu, Yimin Du, Qi An, et al.

This paper introduces VEPO, a new training method that helps AI language models work better with low-resource languages (languages with less training data). The method uses reinforcement learning with built-in quality checks to ensure the model produces properly formatted and grammatically correct outputs, while also dynamically balancing between exact accuracy and natural-sounding responses. Tests show VEPO significantly improves translation quality and efficiency for underrepresented languages.