# A Survey of Generative AI for Detecting Pedophilia Crimes
## 4 Literature Review: AI Approaches for Detection
### Deep Learning and Large Language Models  
This final subsection focuses specifically on research using deep learning architectures, such as Long Short-Term Memory networks (LSTMs) and transformer models such as BERT. It also covers the evaluation and application of LLMs to improve the detection of online grooming behaviors. [PERSON] et al. [26] presented an innovative SPI technique to combat the escalating threat of cybercriminal behavior on social media platforms. Their method integrated sentiment analysis, a novel strategy for generating sentence vectors, and LSTM-RNNs. In this approach, an LSTM-RNN language model generates sentence vectors, which are then utilized by another LSTM-RNN classifier to detect potentially harmful conversations. Fasttext is used to perform sentiment analysis on the conversation content, generating sentiment scores that contribute to the identification of possible predators. The proposed approach exhibits remarkable accuracy and precision, outperforming previous results in the SPI competition. The study utilized the PAN2012 dataset, as well as the IMDB movie review dataset, for training. The study yielded significant results, including a perplexity score of 10.948 for the language model, an impressive 99.43% accuracy in detecting conversations, and effective identification of sexual predators. However, challenges such as noise in the dataset and the inherent complexity of the task remain. The study concludes that the combined approach offers an effective solution for identifying sexual predators in online spaces. [PERSON] et al. [6] explored the potential of automated systems to support human analysts in managing the troubling content associated with crimes such as sextortion, sexting, grooming, and sexual cyberbullying. Recognizing the emotional burden and time-consuming nature of manual analysis, the authors proposed an innovative tool powered by a BERT-based LLM to categorize abuse reports based on subject matter, criminal severity, and harm. This model was trained and evaluated using 1196 real reports sourced from \"Te Protejo\", a Colombian hotline dedicated to combating online child sexual abuse. The study provides a detailed account of the data annotation process, highlighting the critical role of a multidisciplinary team in ensuring both accuracy and sensitivity. To overcome challenges such as data sensitivity and class imbalance, the authors used a data augmentation method, specifically, randomly deleting words from the complaints to create a more diverse set of training examples, thereby increasing the robustness of the model. The results show that fine-tuning the LLM improves classification accuracy and reduces false positive rates, suggesting the potential for such automated tools to make a meaningful contribution to combating online child sexual abuse. [PERSON] and [PERSON] [11] investigated the effectiveness of large LLMs in combating online grooming by assessing their ability to detect such behavior and provide guidance. They evaluated six LLMs, including ChatGPT, PaLM 2, and Claude 2, focusing on how the design of the prompts affected the models' ability to detect grooming and provide appropriate advice to children. The researchers tested the models with 100 prompts based on actual conversations between adults posing as minors and online predators. The results showed that none of the models adequately prevented online grooming. Specifically, closed-source models were found to be overly cautious, while open-source models had a greater risk of generating harmful responses. A major challenge identified was the inconsistency of responses from the LLMs, particularly when handling conversations of moderate to low risk. This variability, even within the same model, underscores the need for ongoing research to refine the accuracy and safety of LLMs in addressing online grooming situations. [PERSON] et al. [27] focused on the early detection of sexual predators in online chats to protect children from digital grooming.