  Pornographic content occurring in human-machine interaction dialogues can
cause severe side effects for users in open-domain dialogue systems. However,
research on detecting pornographic language within human-machine interaction
dialogues is an important subject that is rarely studied. To advance in this
direction, we introduce CensorChat, a dialogue monitoring dataset aimed at
detecting whether the dialogue session contains pornographic content. To this
end, we collect real-life human-machine interaction dialogues in the wild and
break them down into single utterances and single-turn dialogues, with the last
utterance spoken by the chatbot. We propose utilizing knowledge distillation of
large language models to annotate the dataset. Specifically, first, the raw
dataset is annotated by four open-source large language models, with the
majority vote determining the label. Second, we use ChatGPT to update the empty
label from the first step. Third, to ensure the quality of the validation and
test sets, we utilize GPT-4 for label calibration. If the current label does
not match the one generated by GPT-4, we employ a self-criticism strategy to
verify its correctness. Finally, to facilitate the detection of pornographic
text, we develop a series of text classifiers using a pseudo-labeled dataset.
Detailed data analysis demonstrates that leveraging knowledge distillation
techniques with large language models provides a practical and cost-efficient
method for developing pornographic text detectors.
