13 papers
Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications
Yong Yang, Chong Fu, Tong Zhang +6
Large language model (LLM)-based applications rely on system prompts to encode core logic and developer-defined constraints, making these prompts important intellectual property. H…
Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices
Tao Lu, Haoyu Wang, Zonghui Wang +3
With the growing deployment of large language models (LLMs), LLM inference cost has become a key challenge. Pruning techniques that introduce sparsity into weight matrices can acce…
MultiToP: Learning to Patch Visual Tokens to Mitigate Hallucinations in Video Large Multimodal Models
Yuansheng Gao, Wenbin Xing, Jiahao Yuan +4
Video Large Multimodal Models have achieved remarkable progress in video understanding, yet they remain prone to hallucinations, where generated responses are not faithfully suppor…
Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models
Yuansheng Gao, Jinman Zhao, Tong Zhang +5
Although Video Large Multimodal Models have achieved strong performance in video understanding, they still suffer from hallucination. Existing inference-time intervention methods u…
Learning-based Sketches for Frequency Estimation in Data Streams without Ground Truth
Xinyu Yuan, Yan Qiao, Meng Li +4
Estimating the frequency of items on the high-volume, fast data stream has been extensively studied in many areas, such as database and network measurement. Traditional sketches pr…
On the (Generative) Linear Sketching Problem
Xinyu Yuan, Yan Qiao, Zonghui Wang +1
Sketch techniques have been extensively studied in recent years and are especially well-suited to data streaming scenarios, where the sketch summary is updated quickly and compactl…