1 citations · 1 across the 13 of their papers we have counts for
14 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…
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…
Divide, Harmonize, Then Conquer It: Shooting Multi-Commodity Flow Problems with Multimodal Language Models
Xinyu Yuan, Yan Qiao, Zonghui Wang +1
The multi-commodity flow (MCF) problem is a fundamental topic in network flow and combinatorial optimization, with broad applications in transportation, communication, and logistic…
LMTE: Putting the "Reasoning" into WAN Traffic Engineering with Language Models
Xinyu Yuan, Yan Qiao, Zonghui Wang +2
The rapid expansion of modern wide-area networks (WANs) has made traffic engineering (TE) increasingly challenging, as traditional solvers struggle to keep pace. Although existing…
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…
Pre-training CLIP against Data Poisoning with Optimal Transport-based Matching and Alignment
Tong Zhang, Kuofeng Gao, Jiawang Bai +5
Recent studies have shown that Contrastive Language-Image Pre-training (CLIP) models are threatened by targeted data poisoning and backdoor attacks due to massive training image-ca…
VModA: An Effective Framework for Adaptive NSFW Image Moderation
Han Bao, Qinying Wang, Zhi Chen +6
Not Safe/Suitable for Work (NSFW) content is rampant on social networks and poses serious harm to citizens, especially minors. Current detection methods mainly rely on deep learnin…
Boosting Large Language Models for Mental Manipulation Detection via Data Augmentation and Distillation
Yuansheng Gao, Peng Gao, Han Bao +4
Mental manipulation on social media poses a covert yet serious threat to individuals' psychological well-being and the integrity of online interactions. Detecting such behavior is…
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…
An Inversion-based Measure of Memorization for Diffusion Models
Zhe Ma, Qingming Li, Xuhong Zhang +5
The past few years have witnessed substantial advances in image generation powered by diffusion models. However, it was shown that diffusion models are susceptible to training data…
PRSA: Prompt Stealing Attacks against Real-World Prompt Services
Yong Yang, Changjiang Li, Qingming Li +6
Recently, large language models (LLMs) have garnered widespread attention for their exceptional capabilities. Prompts are central to the functionality and performance of LLMs, maki…
Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval
Zhe Ma, Jianfeng Dong, Shouling Ji +7
Visual retrieval aims to search for the most relevant visual items, e.g., images and videos, from a candidate gallery with a given query item. Accuracy and efficiency are two compe…