Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation

Zhexin Zhang, Jiale Cheng, Hao Sun, Jiawen Deng, Fei Mi, Yasheng Wang, Lifeng Shang, Minlie Huang


Abstract
Large pretrained language models can easily produce toxic or biased content, which is prohibitive for practical use. In order to detect such toxic generations, existing methods rely on templates, real-world data extraction, crowdsourcing workers or automatic generation to construct adversarial contexts that are likely to induce toxic generations. However, what type of context is more likely to induce unsafe responses is still under-explored. In this paper, we identify that context toxicity and context category (e.g., profanity, insult, drugs, etc.) are two important factors to cause safety issues in response generation. Hence, we propose a method called reverse generation to construct adversarial contexts conditioned on a given response, with the flexibility to control category, toxicity level and inductivity of the generated contexts. Via reverse generation, we augment the existing BAD dataset and construct a new dataset BAD+ which contains more than 120K diverse and highly inductive contexts in 12 categories. We test three popular pretrained dialogue models (Blender, DialoGPT and Plato2) and find that BAD+ can largely expose their safety problems. Furthermore, we show that BAD+ can greatly enhance the safety of generation, and we reveal the key factors of safety improvement. Our code and dataset is available at https://github.com/thu-coai/Reverse_Generation.
Anthology ID:
2022.findings-emnlp.270
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3684–3697
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.270
DOI:
Bibkey:
Cite (ACL):
Zhexin Zhang, Jiale Cheng, Hao Sun, Jiawen Deng, Fei Mi, Yasheng Wang, Lifeng Shang, and Minlie Huang. 2022. Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3684–3697, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation (Zhang et al., Findings 2022)
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PDF:
https://preview.aclanthology.org/author-url/2022.findings-emnlp.270.pdf