2 citations · 2 across the 3 of their papers we have counts for
12 papers
Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression
Yong Zhang, Heng Li, Yanwen Huang +6
Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts. Existing context compression methods typically rely on heuristic relevance estimation or…
Make Your LVLM KV Cache More Lightweight
Xihao Chen, Yangyang Guo, Roger Zimmermann
Key-Value (KV) cache has become a de facto component of modern Large Vision-Language Models (LVLMs) for inference. While it enhances decoding efficiency in Large Language Models (L…
VidHal: Benchmarking Temporal Hallucinations in Vision LLMs
Wey Yeh Choong, Yangyang Guo, Mohan Kankanhalli
Vision Large Language Models (VLLMs) are widely acknowledged to be prone to hallucinations. Existing research addressing this problem has primarily been confined to image inputs, w…
LLMs Can Unlearn Refusal with Only 1,000 Benign Samples
Yangyang Guo, Ziwei Xu, Si Liu +2
This study reveals a previously unexplored vulnerability in the safety alignment of Large Language Models (LLMs). Existing aligned LLMs predominantly respond to unsafe queries with…
Involuntary Jailbreak: On Self-Prompting Attacks
Yangyang Guo, Yangyan Li, Mohan Kankanhalli
In this study, we disclose a worrying new vulnerability in Large Language Models (LLMs), which we term \textbf{involuntary jailbreak}. Unlike existing jailbreak attacks, this weakn…
FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed
Sizhe Dang, Yangyang Guo, Yanjun Zhao +4
Fine-tuning large language models (LLMs) often faces GPU memory bottlenecks: the backward pass of first-order optimizers like Adam increases memory usage to more than 10 times the…