9 papers
Investigating CoT Monitorability in Large Reasoning Models
Shu Yang, Junchao Wu, Xilin Gong +4
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks by engaging in extended reasoning before producing final answers. Beyond improving abilities…
Benchmarking the Detection of LLMs-Generated Modern Chinese Poetry
Shanshan Wang, Junchao Wu, Fengying Ye +3
The rapid development of advanced large language models (LLMs) has made AI-generated text indistinguishable from human-written text. Previous work on detecting AI-generated text ha…
RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Xin Chen, Junchao Wu, Shu Yang +7
Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well,…
Understanding and Mitigating Political Stance Cross-topic Generalization in Large Language Models
Jiayi Zhang, Shu Yang, Junchao Wu +2
Fine-tuning Large Language Models on a political topic will significantly manipulate their political stance on various issues and unintentionally affect their stance on unrelated t…
Is Long-to-Short a Free Lunch? Investigating Inconsistency and Reasoning Efficiency in LRMs
Shu Yang, Junchao Wu, Xuansheng Wu +3
Large Reasoning Models (LRMs) have achieved remarkable performance on complex tasks by engaging in extended reasoning before producing final answers, yet this strength introduces t…
Understanding Aha Moments: from External Observations to Internal Mechanisms
Shu Yang, Junchao Wu, Xin Chen +4
Large Reasoning Models (LRMs), capable of reasoning through complex problems, have become crucial for tasks like programming, mathematics, and commonsense reasoning. However, a key…