10 papers · 1 filter
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…
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…
Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering
Yinghao Hu, Leilei Gan, Wenyi Xiao +2
Hallucination, or the generation of incorrect or fabricated information, remains a critical challenge in large language models (LLMs), particularly in high-stake domains such as le…
Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator
Chengyuan Liu, Shihang Wang, Lizhi Qing +4
Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs. But it still requires professional knowledge to facilitate the expertise for some…
Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs
Chengyuan Liu, Shihang Wang, Lizhi Qing +4
While Large Language Models (LLMs) demonstrate impressive generation abilities, they frequently struggle when it comes to specialized domains due to their limited domain-specific k…