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We propose a self-correction mechanism for Large Language Models (LLMs) tomitigate issues such as toxicity and fact hallucination. This method involvesrefining model outputs through an ensemble of critics and the model's ownfeedback. Drawing inspiration from human behavior, we explore whether LLMs canemulate the self-correction process observed in humans who often engage inself-reflection and seek input from others to refine their understanding ofcomplex topics. Our approach is model-agnostic and can be applied acrossvarious domains to enhance trustworthiness by addressing fairness, bias, androbustness concerns. We consistently observe performance improvements in LLMsfor reducing toxicity and correcting factual errors.

N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics