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ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

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arxiv 2310.17389 v1 pith:LILDZUIX submitted 2023-10-26 cs.CL cs.AI

Zi Lin , Zihan Wang , Yongqi Tong , Yangkun Wang , Yuxin Guo , Yujia Wang , Jingbo Shang This is my paper · ORCID
classification cs.CLcs.AI
keywords toxicityuser-aidetectiontoxicchatchallengesmodelsreal-worldbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite remarkable advances that large language models have achieved in chatbots, maintaining a non-toxic user-AI interactive environment has become increasingly critical nowadays. However, previous efforts in toxicity detection have been mostly based on benchmarks derived from social media content, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. In this work, we introduce ToxicChat, a novel benchmark based on real user queries from an open-source chatbot. This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference compared to social media content. Our systematic evaluation of models trained on existing toxicity datasets has shown their shortcomings when applied to this unique domain of ToxicChat. Our work illuminates the potentially overlooked challenges of toxicity detection in real-world user-AI conversations. In the future, ToxicChat can be a valuable resource to drive further advancements toward building a safe and healthy environment for user-AI interactions.

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Cited by 11 Pith papers

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