N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics
This addresses trustworthiness issues like fairness, bias, and robustness for users of LLMs, but it is incremental as it builds on existing self-correction ideas.
The paper tackles the problem of toxicity and fact hallucination in Large Language Models by proposing a self-correction mechanism using an ensemble of critics and model feedback, resulting in consistent performance improvements in reducing toxicity and correcting factual errors.
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. We consistently observe performance improvements in LLMs for reducing toxicity and correcting factual errors.