Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction

Understand

Self-reflection for Large Language Models (LLMs) has gained significant attention.

• Existing approaches involve models iterating and improving their previous responses based on LLMs' internal reflection ability or external feedback.
• However, recent research has raised doubts about whether intrinsic self-correction without external feedback may even degrade performance.
• Based on our empirical evidence, we find that current static reflection methods may lead to redundant, drift, and stubborn issues.