3 papers
cs.CL2025
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?
Jingyuan Wang, Yankai Chen, Zhonghang Li +1
Large language models (LLMs) have demonstrated remarkable progress in reasoning, often through supervised fine-tuning (SFT). However, SFT is resource-intensive, relying on large cu…
cs.CV2025
Towards Effective MLLM Jailbreaking Through Balanced On-Topicness and OOD-Intensity
Zuoou Li, Weitong Zhang, Jingyuan Wang +4
Multimodal large language models (MLLMs) are widely used in vision-language reasoning tasks. However, their vulnerability to adversarial prompts remains a serious concern, as safet…
stat.ML2024
Full Bayesian Significance Testing for Neural Networks
Zehua Liu, Zimeng Li, Jingyuan Wang +1
Significance testing aims to determine whether a proposition about the population distribution is the truth or not given observations. However, traditional significance testing oft…