Reward Modeling for Mitigating Toxicity in Transformer-based Language Models

TL;DR

This paper introduces Reinforce-Detoxify, a reinforcement learning approach that improves toxicity mitigation in transformer-based language models by reducing social bias and outperforming existing methods.

Contribution

The paper presents a novel reinforcement learning-based reward model that effectively reduces toxicity and social bias in language model outputs, surpassing previous detoxification techniques.

Findings

Reinforce-Detoxify outperforms existing detox methods in automatic metrics.

The approach reduces unintended social bias in generated content.

The method enhances safety in language model deployment.

Abstract

Transformer-based language models are able to generate fluent text and be efficiently adapted across various natural language generation tasks. However, language models that are pretrained on large unlabeled web text corpora have been shown to suffer from degenerating toxic content and social bias behaviors, consequently hindering their safe deployment. Various detoxification methods were proposed to mitigate the language model's toxicity; however, these methods struggled to detoxify language models when conditioned on prompts that contain specific social identities related to gender, race, or religion. In this study, we propose Reinforce-Detoxify; A reinforcement learning-based method for mitigating toxicity in language models. We address the challenge of safety in language models and propose a new reward model that is able to detect toxic content and mitigate unintended bias towards social identities in toxicity prediction. The experiments demonstrate that the Reinforce-Detoxify method for language model detoxification outperforms existing detoxification approaches in automatic evaluation metrics, indicating the ability of our approach in language model detoxification and less prone to unintended bias toward social identities in generated content.