Benchmark for Detecting Child Pornography in Open Domain Dialogues Using Large Language Models

As large language models become increasingly prevalent, their safe and secure application, particularly in preventing the generation of child pornographic content in real-world open-domain dialogues, has become a crucial concern. Despite the urgency of this issue, research efforts are hindered by the lack of dedicated datasets for this area. Addressing this gap, we introduce a pioneering benchmark dataset specifically designed for the detection of child pornography in open-domain dialogues. Recognizing the intrinsic complexities involved in labling such data, we developed a novel Distillation-Based Recurrent Extraction method. This approach enables us to efficiently gather, annotate, and refine the data collection process. Our dataset categorizes dialogues into three distinct sections: non-pornographic, child pornographic, and adult pornographic, ensuring clear differentiation between child and adult content. Through extensive experiments, we demonstrate that LLMs including BERT, RoBERTa, LLaMA, among others, significantly enhance their detection capabilities when fine-tuned with our dataset. This improvement not only attests to the dataset’s immediate utility but also highlights its importance for guiding future research. Furthermore, our findings indicate substantial potential for further advancements in detection performance, emphasizing the critical role of our benchmark dataset in ongoing research efforts.