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

Understand

We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination.

• This method involves refining model outputs through an ensemble of critics and the model's own feedback.
• Drawing inspiration from human behavior, we explore whether LLMs can emulate the self-correction process observed in humans who often engage in self-reflection and seek input from others to refine their understanding of complex topics.
• Our approach is model-agnostic and can be applied across various domains to enhance trustworthiness by addressing fairness, bias, and robustness concerns.