Self-reflection enables language models to identify and fix problematic responses, significantly improving safety and reducing bias.

• Reduced toxic content by 75.8% while preserving appropriate responses
• Decreased political bias in model outputs
• Required minimal computational overhead compared to other safety techniques
• Maintained performance on standard benchmarks

This research provides a practical approach for enhancing AI safety without compromising functionality, addressing a critical need in enterprise AI deployment where harmful outputs can create significant risks.

Self-Reflection Makes Large Language Models Safer, Less Biased, and Ideologically Neutral