1 citations · 2 across the 16 of their papers we have counts for
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Uncovering Uncontrolled Repetition through Residual Stream Dynamics
Yuanhe Zhang, Xinyao Zhou, Haoran Gao +5
Uncontrolled repetition can prolong autoregressive generation in large language models (LLMs) and enable resource consumption attacks. Prior analyses of repetitive generation have…
From Helpfulness to Toxic Proactivity: Diagnosing Behavioral Misalignment in LLM Agents
Xinyue Wang, Yuanhe Zhang, Zhengshuo Gong +6
The enhanced capabilities of LLM-based agents come with an emergency for model planning and tool-use abilities. Attributing to helpful-harmless trade-off from LLM alignment, agents…
LIFEBench: Evaluating Length Instruction Following in Large Language Models
Wei Zhang, Zhenhong Zhou, Kun Wang +9
While large language models (LLMs) can solve PhD-level reasoning problems over long context inputs, they still struggle with a seemingly simpler task: following explicit length ins…
Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings
Yuanhe Zhang, Zhenhong Zhou, Wei Zhang +4
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks yet still are vulnerable to external threats, particularly LLM Denial-of-Service (LLM-DoS…
Alignment-Enhanced Decoding:Defending via Token-Level Adaptive Refining of Probability Distributions
Quan Liu, Zhenhong Zhou, Longzhu He +3
Large language models are susceptible to jailbreak attacks, which can result in the generation of harmful content. While prior defenses mitigate these risks by perturbing or inspec…
Speak Out of Turn: Safety Vulnerability of Large Language Models in Multi-turn Dialogue
Zhenhong Zhou, Jiuyang Xiang, Haopeng Chen +3
Large Language Models (LLMs) have been demonstrated to generate illegal or unethical responses, particularly when subjected to "jailbreak." Research on jailbreak has highlighted th…
Quantifying and Analyzing Entity-level Memorization in Large Language Models
Zhenhong Zhou, Jiuyang Xiang, Chaomeng Chen +1
Large language models (LLMs) have been proven capable of memorizing their training data, which can be extracted through specifically designed prompts. As the scale of datasets cont…