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Computer Science > Computation and Language

arXiv:2203.03072 (cs)
[Submitted on 6 Mar 2022]

Title:Leashing the Inner Demons: Self-Detoxification for Language Models

Authors:Canwen Xu, Zexue He, Zhankui He, Julian McAuley
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Abstract: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.
Comments: AAAI 2022
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2203.03072 [cs.CL]
  (or arXiv:2203.03072v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2203.03072

Submission history

From: Canwen Xu [view email]
[v1] Sun, 6 Mar 2022 23:55:12 UTC (169 KB)
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