10 papers
DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery
Zhengkang Guan, Yikang Chen, Yi He +5
Causal discovery is critical for understanding complex data-generating mechanisms, yet traditional algorithms often struggle with highly non-linear and noisy systems, or suffer fro…
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…
Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond
Yinghao Hu, Yaoyao Yu, Leilei Gan +3
Recent advances in test-time scaling of large language models (LLMs), exemplified by DeepSeek-R1 and OpenAI's o1, show that extending the chain of thought during inference can sign…
Causal Agent based on Large Language Model
Kairong Han, Kun Kuang, Ziyu Zhao +2
The large language model (LLM) has achieved significant success across various domains. However, the inherent complexity of causal problems and causal theory poses challenges in ac…
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…
Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction
Yuting Huang, Chengyuan Liu, Yifeng Feng +4
As Large Language Models (LLMs) are widely applied in various domains, the safety of LLMs is increasingly attracting attention to avoid their powerful capabilities being misused. E…