# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Deep Learning and Large Language Models
The authors aimed to develop a system capable of analyzing ongoing chat conversations in real-time, detecting grooming attempts, and intervening before these interactions escalate into sexual abuse. To train and evaluate their system, the researchers developed a novel dataset called PANC, which merged data from two existing datasets: PAN12 and ChatCoder2. The system uses a BERT model to examine sliding windows of chat messages and classifies each window as predatory or non-predatory. Among the models tested, the BERT-base model showed superior performance in early detection. The BERT-base and BERT-large models outperformed current state-of-the-art methods in non-early detection scenarios. The effectiveness of the models was evaluated using the F1 score, precision, recall, and processing speed. Despite challenges such as obtaining authentic grooming chat data and the lack of fully representative negative chats in the dataset, the research demonstrated the viability of using BERT models for early detection of sexual predators. [PERSON] et al. [28] investigated the fine-tuning of the BERT language model for detecting cyber grooming, with a specific focus on how informal language impacts its performance. The primary objective was to assess whether BERT could be trained to understand abbreviations and slang within predatory online chats and whether replacing such language with formal text would improve detection rates. Using the PyTorch framework, various models including BERT-base, RoBERTa, and ALBERT were fine-tuned on the PAN12 and AIBA AS datasets, which contain both formal and highly informal conversations. Methodologies also included data augmentation through techniques like back-translation to address significant data imbalances. The study evaluated model performance using precision, recall, and F1-score as its primary metrics for accuracy. The BERT-base model demonstrated strong performance, achieving a top F1-score of 0.86 when trained on the full PAN12 dataset. A critical finding was that its performance remained comparable on subsets of the formal PAN12 data (0.63 F1-score) and the highly informal AIBA AS data (0.62 F1-score), indicating a high tolerance for slang. The research concluded that replacing abbreviations degraded performance and that BERT's architecture provides a robust understanding of language without extensive pre-processing. Key challenges included data imbalance and the variability of slang. Future work should focus on methods to add extra weight to high-risk keywords and create more advanced systems for translating informal language. [PERSON] et al. [29] introduced a contrastive learning framework for sentence-based feature extraction to identify predatory online conversations, effectively handling misspellings. Its core methodology utilized RoBERTa encoders and supervised SimCSE to train an SVM classifier, achieving 0.99 accuracy and a 0.96 F\({}_{0.5}\) Score. The study aimed to develop a robust grooming detection system using the PAN 2012 dataset, with feature extraction performed via SimCSE (utilizing RoBERTa/BERT) and classification achieved through SVM and other models. Fusion techniques, like the sum rule, further boosted performance to 0.99 accuracy and a 0.98 F\({}_{0.5}\)-score. The research highlights the efficacy of the RoBERTa-SimCSE-SVM setup and ensemble fusion, emphasizing the F\({}_{0.5}\)-score's law enforcement relevance. It also notes challenges like context dependency and data imbalance, concluding that sentence-level embeddings are superior. [PERSON] et al. [30] investigated the detection of online sexual predatory chats and abusive language using the Llama 2 large language model. Their approach focused on fine-tuning the pre-trained Llama 2 7B-parameter model with different datasets that varied in size, language, and degree of imbalance.