6 papers
Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation
Qingsong Wang, Tao Wu, Wang Lin +4
Large Language Models (LLMs) have demonstrated strong performance in open-ended generation tasks. However, they often struggle to adapt content to users with differing cognitive ca…
Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
Tao Wu, Jingyuan Chen, Wang Lin +6
Distractors-incorrect yet plausible answer choices in multiple-choice questions (MCQs)-are vital in educational assessments, as they help identify student misconceptions by present…
Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents
Tao Wu, Jingyuan Chen, Wang Lin +5
Large language models (LLMs) are revolutionizing education, with LLM-based agents playing a key role in simulating student behavior. A major challenge in student simulation is mode…
WisdomBot: Tuning Large Language Models with Artificial Intelligence Knowledge
Jingyuan Chen, Tao Wu, Wei Ji +1
Large language models (LLMs) have emerged as powerful tools in natural language processing (NLP), showing a promising future of artificial generated intelligence (AGI). Despite the…
AutoGeo: Automating Geometric Image Dataset Creation for Enhanced Geometry Understanding
Zihan Huang, Tao Wu, Wang Lin +3
With the rapid advancement of large language models, there has been a growing interest in their capabilities in mathematical reasoning. However, existing research has primarily foc…
Semantic Alignment for Multimodal Large Language Models
Tao Wu, Mengze Li, Jingyuan Chen +6
Research on Multi-modal Large Language Models (MLLMs) towards the multi-image cross-modal instruction has received increasing attention and made significant progress, particularly…