Recent advancements in mitigating hallucinations in large vision-language models have been highlighted through various innovative frameworks. A Unified Hallucination Mitigation Framework has been proposed to address hallucinations in these models. Another method focuses on dynamically triggering retrieval augmentation based on real-time hallucination detection. The TextGrad framework has shown significant improvements in multimodal large language models (LLMs), particularly in GPT-4v and GPT-4o. TextGrad optimized prompts have increased the accuracy of GPT-4v from 71% to 76% in MMVP (multiple-choice questions), and improved the accuracy of GPT-4o from 77.2% to 82.5% in HQH (open-ended generation tasks). TextGrad advances multimodal reasoning via natural language gradients. These advancements are expected to enhance the reliability of visual-language AI systems.