@inproceedings{
mousavi2023enhancing,
title={Enhancing Large Language Models with Ensemble of Critics for Mitigating Toxicity and Hallucination},
author={Sajad Mousavi and Ricardo Luna Gutierrez and Desik Rengarajan and Vineet Gundecha and Ashwin Ramesh Babu and Avisek Naug and Antonio Guillen and Soumyendu Sarkar},
booktitle={R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models},
year={2023},
url={https://openreview.net/forum?id=4uiOPSvbN6}
}
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.
Enhancing Large Language Models with Ensemble of Critics for Mitigating Toxicity and Hallucination
Sajad Mousavi Ricardo Luna Gutierrez Desik Rengarajan Vineet Gundecha Ashwin Ramesh Babu Avisek Naug Antonio Guillen Soumyendu Sarkar