11 citations · 29 across the 11 of their papers we have counts for
11 papers
Target-driven Attack for Large Language Models
Chong Zhang, Mingyu Jin, Dong Shu +3
Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions…
Better Sampling, towards Better End-to-end Small Object Detection
Zile Huang, Chong Zhang, Mingyu Jin +3
While deep learning-based general object detection has made significant strides in recent years, the effectiveness and efficiency of small object detection remain unsatisfactory. T…
Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities
Wenyue Hua, Kaijie Zhu, Lingyao Li +7
This study intends to systematically disentangle pure logic reasoning and text understanding by investigating the contrast across abstract and contextualized logical problems from…
Invisible Backdoor Attack against Self-supervised Learning
Hanrong Zhang, Zhenting Wang, Boheng Li +7
Self-supervised learning (SSL) models are vulnerable to backdoor attacks. Existing backdoor attacks that are effective in SSL often involve noticeable triggers, like colored patche…
ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction
Mingyu Jin, Haochen Xue, Zhenting Wang +5
The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly…
BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis
Shuhang Lin, Wenyue Hua, Lingyao Li +7
This paper presents BattleAgent, an emulation system that combines the Large Vision-Language Model and Multi-agent System. This novel system aims to simulate complex dynamic intera…
Goal-guided Generative Prompt Injection Attack on Large Language Models
Chong Zhang, Mingyu Jin, Qinkai Yu +3
Current large language models (LLMs) provide a strong foundation for large-scale user-oriented natural language tasks. A large number of users can easily inject adversarial text or…
Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities
Hua Tang, Chong Zhang, Mingyu Jin +5
Large language models (LLMs) have been applied in many fields and have developed rapidly in recent years. As a classic machine learning task, time series forecasting has recently b…
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
Qinkai Yu, Mingyu Jin, Dong Shu +8
Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in int…
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Chong Zhang, Mingyu Jin, Qinkai Yu +3
Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training. However, GZSL methods are prone to bia…
A Simple and Effective Baseline for Attentional Generative Adversarial Networks
Mingyu Jin, Chong Zhang, Qinkai Yu +3
Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnG…