5 papers
Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
Yanming Mu, Hao Hu, Feiyang Li +7
Retrieval-Augmented Generation (RAG) significantly mitigates the hallucinations and domain knowledge deficiency in large language models by incorporating external knowledge bases.…
Fake-in-Facext: Towards Fine-Grained Explainable DeepFake Analysis
Lixiong Qin, Yang Zhang, Mei Wang +3
The advancement of Multimodal Large Language Models (MLLMs) has bridged the gap between vision and language tasks, enabling the implementation of Explainable DeepFake Analysis (XDF…
Face-Human-Bench: A Comprehensive Benchmark of Face and Human Understanding for Multi-modal Assistants
Lixiong Qin, Shilong Ou, Miaoxuan Zhang +6
Faces and humans are crucial elements in social interaction and are widely included in everyday photos and videos. Therefore, a deep understanding of faces and humans will enable m…
POPri: Private Federated Learning using Preference-Optimized Synthetic Data
Charlie Hou, Mei-Yu Wang, Yige Zhu +2
In practical settings, differentially private Federated learning (DP-FL) is the dominant method for training models from private, on-device client data. Recent work has suggested t…
Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting
Yifan Luo, Zhennan Zhou, Meitan Wang +1
In this paper, we investigate the safety mechanisms of instruction fine-tuned large language models (LLMs). We discover that re-weighting MLP neurons can significantly compromise a…