Leashing the Inner Demons: Self-Detoxification for Language Models

No. 10: AAAI-22 Technical Tracks 10

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 36

AAAI Technical Track on Speech and Natural Language Processing

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 unsupervised 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.

10.1609/aaai.v36i10.21406