1 citations · 1 across the 11 of their papers we have counts for
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DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation
Pingzhi Li, Zhen Tan, Mohan Zhang +3
Large Language Models (LLMs) represent substantial intellectual and economic investments, yet their effectiveness can inadvertently facilitate model imitation via knowledge distill…
The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation
Ruichen Zhang, Rana Muhammad Shahroz Khan, Zhen Tan +3
Data-centric distillation, including data augmentation, selection, and mixing, offers a promising path to creating smaller, more efficient student Large Language Models (LLMs) that…
ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion
Rana Muhammad Shahroz Khan, Dongwen Tang, Pingzhi Li +2
Parameter generation has emerged as a novel paradigm for neural network development, offering an alternative to traditional neural network training by synthesizing high-quality mod…
: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks
Rana Muhammad Shahroz Khan, Zhen Tan, Sukwon Yun +2
Most discussions about Large Language Model (LLM) safety have focused on single-agent settings but multi-agent LLM systems now create novel adversarial risks because their behavior…