N-critics: Self-refinement Of Large Language Models With Ensemble Of Critics

Mousavi Sajad, Gutiérrez Ricardo Luna, Rengarajan Desik, Gundecha Vineet, Babu Ashwin Ramesh, Naug Avisek, Guillen Antonio, Sarkar Soumyendu. NeurIPS 2023

[Paper]    
Bias Mitigation Ethics And Bias Fairness Security

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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