Abstract
Despite advances in large pre-trained neural language models, they are prone to generating toxic language, which brings security risks to their applications. We introduce MIL-Decoding, which detoxifies language models at token-level by interpolating it with a trained multiple instance learning (MIL) network.MIL model is trained on a corpus with a toxicity label for each text to predict the overall toxicity and the toxicity of each token in its context. Intuitively, the MIL network computes a toxicity distribution over next tokens according to the generated context which supplements the original language model to avoid toxicity. We evaluate MIL-Decoding with automatic metrics and human evaluation, where MIL-Decoding outperforms other baselines in detoxification while it only hurts generation fluency a little bit.- Anthology ID:
- 2023.acl-long.11
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 190–202
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.11
- DOI:
- 10.18653/v1/2023.acl-long.11
- Cite (ACL):
- Xu Zhang and Xiaojun Wan. 2023. MIL-Decoding: Detoxifying Language Models at Token-Level via Multiple Instance Learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 190–202, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- MIL-Decoding: Detoxifying Language Models at Token-Level via Multiple Instance Learning (Zhang & Wan, ACL 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.acl-long.11.pdf