42 citations · 139 across the 48 of their papers we have counts for
8 papers · 1 filter
Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Models
Yichen Sun, Zhixuan Chu, Zhan Qin +1
The rapid advancement of Text-to-Image(T2I) generative models has enabled the synthesis of high-quality images guided by textual descriptions. Despite this significant progress, th…
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
Lei Liu, Xiaoyan Yang, Junchi Lei +6
With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the poten…
Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models
Zhixuan Chu, Lei Zhang, Yichen Sun +4
The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis of high-fidelity video content guided by textual descriptions. Despite this significant pro…
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution
Shuo Shao, Yiming Li, Hongwei Yao +3
Ownership verification is currently the most critical and widely adopted post-hoc method to safeguard model copyright. In general, model owners exploit it to identify whether a giv…
A Causal Explainable Guardrails for Large Language Models
Zhixuan Chu, Yan Wang, Longfei Li +3
Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for steer…
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification
Yiling He, Junchi Lei, Zhan Qin +2
Machine learning-based Android malware classifiers achieve high accuracy in stationary environments but struggle with concept drift. The rapid evolution of malware, especially with…
Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning
Z Liu, J Lou, W Bao +4
Fine-tuning on task-specific datasets is a widely-embraced paradigm of harnessing the powerful capability of pretrained LLMs for various downstream tasks. Due to the popularity of…
LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation
Zhixuan Chu, Yan Wang, Qing Cui +4
As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully captu…