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

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

URL: http://arxiv.org/abs/2310.18679v2
Date: Wed, 8 Nov 2023 13:23:20 GMT
Title: N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics
Authors: Sajad Mousavi, Ricardo Luna Guti\'errez, Desik Rengarajan, Vineet Gundecha, Ashwin Ramesh Babu, Avisek Naug, Antonio Guillen, Soumyendu Sarkar
Abstract summary: 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.
Score: 5.516095889257118
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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