Self Reflection

Self-reflection in artificial intelligence focuses on enabling AI models, primarily large language models (LLMs), to critically evaluate their own outputs and improve their reasoning processes. Current research emphasizes prompt engineering techniques to guide this self-assessment, exploring methods like dual learning feedback and multi-perspective reflection to enhance accuracy and avoid biases. This work is significant for improving the reliability and trustworthiness of LLMs across diverse applications, from software development and machine translation to decision support systems and even evaluating AI's potential moral status.