9 papers
CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization
Min Zhang, Yuyin Wang, Zhongxiang Dai +4
Recent advances in pre-training vision-language models (VLMs), e.g., contrastive language-image pre-training (CLIP) methods, have shown great potential in learning out-of-distribut…
UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models
Shouang Wei, Min Zhang, Xin Lin +3
Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings, yet current supervised fine-tuning methods only learn surface teachin…
SMRC: Aligning Large Language Models with Student Reasoning for Mathematical Error Correction
Biaojie Zeng, Min Zhang, Juan Zhou +3
Large language models (LLMs) often make reasoning errors when solving mathematical problems, and how to automatically detect and correct these errors has become an important resear…
EduAgentQG: A Multi-Agent Workflow Framework for Personalized Question Generation
Rui Jia, Min Zhang, Fengrui Liu +3
High-quality personalized question banks are crucial for supporting adaptive learning and individualized assessment. Manually designing questions is time-consuming and often fails…
OmniEduBench: A Comprehensive Chinese Benchmark for Evaluating Large Language Models in Education
Min Zhang, Hao Chen, Wenqi Zhang +6
With the rapid development of large language models (LLMs), various LLM-based works have been widely applied in educational fields. However, most existing LLMs and their benchmarks…
EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus
Shouang Wei, Min Zhang, Xin Lin +3
Recently, several multi-turn dialogue benchmarks have been proposed to evaluate the conversational abilities of large language models (LLMs). As LLMs are increasingly recognized as…