Can Multi-Modal (Reasoning) LLMs Detect Document Manipulation?

Document fraud poses a significant threat to industries reliant on secure and verifiable documentation, necessitating robust detection mechanisms. This study investigates the efficacy of state-of-the-art multi-modal large language models LLMs-including OpenAI O1, OpenAI 4o, Gemini Flash thinking,...

When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-Of-Distribution Trap Is All You Need

Data-free knowledge distillation DFKD transfers knowledge from a teacher to a student without access the real in-distribution ID data. Its common solution is to use a generator to synthesize fake data and use them as a substitute for real ID data. However, existing works typically assume teachers...

AiXamine: Simplified LLM Safety and Security

Evaluating Large Language Models LLMs for safety and security remains a complex task, often requiring users to navigate a fragmented landscape of ad hoc benchmarks, datasets, metrics, and reporting formats. To address this challenge, we present aiXamine, a comprehensive black-box evaluation...