2 citations · 2 across the 3 of their papers we have counts for
5 papers
Guided Collaboration in Heterogeneous LLM-Based Multi-Agent Systems via Entropy-Based Understanding Assessment and Experience Retrieval
Linlin Wang, Tianqing Zhu, Laiqiao Qin +2
With recent breakthroughs in large language models (LLMs) for reasoning, planning, and complex task generation, artificial intelligence systems are transitioning from isolated sing…
Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs
Linlin Wang, Tianqing Zhu, Laiqiao Qin +2
In Large Language Models, Retrieval-Augmented Generation (RAG) systems can significantly enhance the performance of large language models by integrating external knowledge. However…
Safe and Reliable Diffusion Models via Subspace Projection
Huiqiang Chen, Tianqing Zhu, Linlin Wang +3
Large-scale text-to-image (T2I) diffusion models have revolutionized image generation, enabling the synthesis of highly detailed visuals from textual descriptions. However, these m…
Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA
Laiqiao Qin, Tianqing Zhu, Linlin Wang +1
Machine unlearning is an emerging technology that removes a subset of the training data from a trained model without significantly affecting the model performance on the remaining…
Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives
Linlin Wang, Tianqing Zhu, Wanlei Zhou +1
Federated learning is fast becoming a popular paradigm for applications involving mobile devices, banking systems, healthcare, and IoT systems. Hence, over the past five years, res…