Leashing the Inner Demons: Self-Detoxification for Language Models
This addresses safety risks for practical LM applications, though it appears incremental as it builds on existing detoxification approaches.
The paper tackles the problem of language models reproducing toxic content from training data by proposing a self-detoxification method that reduces toxicity in generated text without needing external resources, showing better toxicity reduction than a supervised baseline while maintaining generation quality.
Language models (LMs) can reproduce (or amplify) toxic language seen during training, which poses a risk to their practical application. In this paper, we conduct extensive experiments to study this phenomenon. We analyze the impact of prompts, decoding strategies and training corpora on the output toxicity. Based on our findings, we propose a simple yet effective method for language models to "detoxify" themselves without an additional large corpus or external discriminator. Compared to a supervised baseline, our proposed method shows better toxicity reduction with good generation quality in the generated content under multiple settings. Warning: some examples shown in the paper may contain uncensored offensive content.