Publications (45)
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
Xinlu Zhang, Shiyang Li, Xianjun Yang +3
Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments. Although offering improved data…
On explosive boiling of a multicomponent Leidenfrost drop
Sijia Lyu, Huanshu Tan, Yuki Wakata +4
The gasification of multicomponent fuel drops is relevant in various energy-related technologies. An interesting phenomenon associated with this process is the self-induced explosi…
Your thoughts tell who you are: Characterize the reasoning patterns of LRMs
Yida Chen, Yuning Mao, Xianjun Yang +7
Current comparisons of large reasoning models (LRMs) focus on macro-level statistics such as task accuracy or reasoning length. Whether different LRMs reason differently remains an…
Weak-to-Strong Jailbreaking on Large Language Models
Xuandong Zhao, Xianjun Yang, Tianyu Pang +4
Large language models (LLMs) are vulnerable to jailbreak attacks - resulting in harmful, unethical, or biased text generations. However, existing jailbreaking methods are computati…
AlpaCare:Instruction-tuned Large Language Models for Medical Application
Xinlu Zhang, Chenxin Tian, Xianjun Yang +3
Instruction-finetuning (IFT) has become crucial in aligning Large Language Models (LLMs) with diverse human needs and has shown great potential in medical applications. However, pr…
Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models
Hoang Phan, Xianjun Yang, Yuanshun Yao +6
Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for c…