5 papers
Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
Zhenyuan Guo, Tong Chen, Wenlong Meng +4
Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer. However, these extended generations inc…
Enhancing Meme Emotion Understanding with Multi-Level Modality Enhancement and Dual-Stage Modal Fusion
Yi Shi, Wenlong Meng, Zhenyuan Guo +2
With the rapid rise of social media and Internet culture, memes have become a popular medium for expressing emotional tendencies. This has sparked growing interest in Meme Emotion…
R.R.: Unveiling LLM Training Privacy through Recollection and Ranking
Wenlong Meng, Zhenyuan Guo, Lenan Wu +5
Large Language Models (LLMs) pose significant privacy risks, potentially leaking training data due to implicit memorization. Existing privacy attacks primarily focus on membership…
Dialogue Injection Attack: Jailbreaking LLMs through Context Manipulation
Wenlong Meng, Fan Zhang, Wendao Yao +4
Large language models (LLMs) have demonstrated significant utility in a wide range of applications; however, their deployment is plagued by security vulnerabilities, notably jailbr…
Be Cautious When Merging Unfamiliar LLMs: A Phishing Model Capable of Stealing Privacy
Zhenyuan Guo, Yi Shi, Wenlong Meng +3
Model merging is a widespread technology in large language models (LLMs) that integrates multiple task-specific LLMs into a unified one, enabling the merged model to inherit the sp…