Self Reflective Prompting

Self-reflective prompting is a technique that enhances the performance of large language models (LLMs) by allowing them to generate and utilize their own prompts, improving reasoning and reducing reliance on external, expert-provided guidance. Current research focuses on applying this technique to various tasks, including relation extraction, image segmentation, and question answering, often employing iterative refinement processes and incorporating self-distillation or query-disentangling methods within LLMs and visual foundation models (VFMs) like SAM. This approach shows promise in improving the zero-shot and few-shot capabilities of these models, leading to more robust and adaptable AI systems across diverse domains, particularly in areas like medical image analysis where expert-labeled data is scarce. The ultimate goal is to create more autonomous and effective AI systems that require less human intervention.