1 citations · 1 across the 18 of their papers we have counts for
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EchoDistill: Robust Large Audio Language Models via Noisy-to-Clean Self-Distillation
Liang Lin, Chunxi Luo, Kaiwen Luo +12
Large Audio Language Models (LALMs) remain vulnerable to acoustic noise, which can obscure task-relevant evidence and produce unreliable responses. We propose EchoDistill, a noisy-…
LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models
Wei Zhang, Lintong Du, Yuanhe Zhang +4
Despite the strong performance of Large Language Models (LLMs) on complex instruction-following tasks, precise control of output length remains a persistent challenge. Existing met…
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…
Jailbreaking Large Language Diffusion Models: Revealing Hidden Safety Flaws in Diffusion-Based Text Generation
Yuanhe Zhang, Fangzhou Xie, Zhenhong Zhou +4
Large Language Diffusion Models (LLDMs) exhibit comparable performance to LLMs while offering distinct advantages in inference speed and mathematical reasoning tasks.The precise an…
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…
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models
Zhenhong Zhou, Zherui Li, Jie Zhang +4
Large Language Model-based Multi-Agent Systems (LLM-MASs) have demonstrated remarkable real-world capabilities, effectively collaborating to complete complex tasks. While these sys…
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…