8 papers
SafeEditor: Unified MLLM for Efficient Post-hoc T2I Safety Editing
Ruiyang Zhang, Jiahao Luo, Xiaoru Feng +3
With the rapid advancement of text-to-image (T2I) models, ensuring their safety has become increasingly critical. Existing safety approaches can be categorized into training-time a…
SafeMT: Multi-turn Safety for Multimodal Language Models
Han Zhu, Juntao Dai, Jiaming Ji +8
With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interacti…
Perception, Understanding and Reasoning, A Multimodal Benchmark for Video Fake News Detection
Cui Yakun, Peng Qi, Fushuo Huo +6
The advent of multi-modal large language models (MLLMs) has greatly advanced research on video fake news detection (VFND) tasks. Existing benchmarks typically focus on the detectio…
A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus in LLMs
Guoxi Zhang, Jiawei Chen, Tianzhuo Yang +3
The increasing prevalence of large language models (LLMs) is influencing global value systems. However, these models frequently exhibit a pronounced WEIRD (Western, Educated, Indus…
SafeLawBench: Towards Safe Alignment of Large Language Models
Chuxue Cao, Han Zhu, Jiaming Ji +7
With the growing prevalence of large language models (LLMs), the safety of LLMs has raised significant concerns. However, there is still a lack of definitive standards for evaluati…
Mitigating Deceptive Alignment via Self-Monitoring
Jiaming Ji, Wenqi Chen, Kaile Wang +8
Modern large language models rely on chain-of-thought (CoT) reasoning to achieve impressive performance, yet the same mechanism can amplify deceptive alignment, situations in which…