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Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the generated texts do not align with the given contexts, significantly restricting the usages of LVLMs.
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System
We anticipate that our work will contribute to the advancement of research on TrainerAgent in both academic and industry communities, potentially establishing it as a new paradigm for model development in the field of AI.
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
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