To further improve the hallucination-based augmentation technique for generating more realistic and relevant additional information for video and text data, several strategies can be considered. Firstly, incorporating domain-specific knowledge or constraints into the hallucination process can help ensure that the generated information aligns with the context of the data. This can involve conditioning the hallucination process on specific attributes or features relevant to the domain of the data. Additionally, exploring advanced generative models that focus on capturing fine-grained details and nuances in the data can enhance the realism of the generated information. Techniques like adversarial training or reinforcement learning can be employed to refine the hallucination process and generate more coherent and contextually relevant data. Moreover, leveraging feedback mechanisms from downstream tasks or human annotators can provide valuable insights for improving the quality and relevance of the hallucinated information. By iteratively refining the hallucination process based on feedback and domain knowledge, the technique can be enhanced to generate more realistic and meaningful additional information for video and text data.