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

Published: 07 Nov 2023, Last Modified: 23 Nov 2023FMDM@NeurIPS2023EveryoneRevisionsBibTeX
Keywords: LLM, Toxicity, Hallucination, Self refinement, Self correction, ensemble of critics
TL;DR: We propose a model-agnostic self-correction framework for Large Language Models (LLMs) that utilizes an ensemble of critics inspired by human self-reflection to address issues like toxicity and hallucination.
Abstract: 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.
Submission Number: 8