3 accepted papers
Hallucination remains a critical challenge for multimodal large language models (MLLMs), undermining their reliability in real-world applications. While fine-grained hallucination detection (FHD) holds promise for enhancing high-quality vision-language data construction and model alignment through e…
Text-to-image (T2I) diffusion models have achieved impressive image generation quality and are increasingly fine-tuned for personalized applications. However, these models often inherit unsafe behaviors from toxic pretraining data, raising growing safety concerns. While recent safety-driven unlearni…
The risk of harmful contents generated by large language models (LLMs) becomes a critical concern. This paper systematically evaluates and enhances LLMs’ capability to perform course-correction, , the model can steer away from generating harmful content autonomously. First, we introduce the C2-Eval…