Domain Invariant Prompt

Domain-invariant prompts aim to improve the generalizability of large language and vision-language models across diverse datasets by learning prompts that are effective regardless of the specific domain. Current research focuses on optimizing prompt design and learning strategies, including soft and hard prompt tuning, gradient alignment, and memory-efficient techniques, often leveraging pre-trained models like CLIP and adapting them for specific tasks through prompt engineering. This work is significant because it addresses the limitations of models trained on single domains, leading to improved performance and robustness in real-world applications where data heterogeneity is common, such as in medical image analysis and robotics. The development of domain-invariant prompts enhances the adaptability and efficiency of large models, reducing the need for extensive retraining on new datasets.