# A Survey of Generative AI for Detecting Pedophilia Crimes
## 2 Background
### Large Language Models  
An LLM is an advanced deep learning algorithm that is trained on large amounts of text data. This training gives LLMs the ability to [11]:  
* Understand and generate human-like text: LLMs can participate in conversations, answer questions, summarize text, and even mimic different writing styles;
* Identify patterns and connections in language: This enables them to detect topics, sentiments, and potential warning signs in online exchanges;* Learn and adapt: LLMs can be fine-tuned to specific datasets, such as conversations related to online grooming, to improve their accuracy in detecting suspicious behavior.
* While platforms like Perplexity AI, Google Gemini, and ChatGPT are primarily engineered for general-purpose information retrieval and dialogue generation, their underlying LLM architectures present valuable opportunities for specialized applications such as crime detection and child protection. Google Gemini, with its extensive context window and multimodal capabilities, enables the analysis of lengthy, nuanced conversations and supports the interpretation of diverse content formats--including text, images, and video--making it particularly effective in the detection of long-term grooming behavior and CSAM [6]. ChatGPT, known for its adaptability via fine-tuning, is well-suited for domain-specific implementations, such as detecting linguistic cues indicative of grooming or facilitating interactive simulation environments for proactive threat detection. Perplexity AI, leveraging real-time search integration and transparent source attribution, offers substantial potential for monitoring live online forums and chat spaces, where grooming often manifests. Collectively, these tools--when used with awareness of their distinct capabilities and limitations, can inform the development of more targeted and effective generative AI-driven safety systems.
* Regarding data retrieval, ChatGPT depends solely on its static training corpus, whereas both Gemini and Perplexity AI utilize dynamic search-based retrieval mechanisms. Gemini stands out with a significantly larger context window than the other two, allowing it to process and analyze complex documents in greater depth. Additionally, Gemini's multimodal processing sets it apart from the primarily text-based functionalities of ChatGPT and Perplexity. Cost and accessibility also vary: ChatGPT offers a freemium model, Perplexity remains free, and Gemini's pricing is tiered based on model version and access features.  
It is important to understand how models with generative origins are used in detection tasks. Although these models, including LLMs, can generate new content, they are most effective in detection scenarios due to their sophisticated understanding of language, context, and patterns. In practice, a pre-trained generative model, such as BERT or Llama 2, is often used as a powerful feature extractor. Then, the model is fine-tuned on a specific dataset for a discriminative task, such as classifying a conversation as 'grooming' or 'non-grooming'. In this context, \"Generative AI\" does not necessarily refer to generating content for detection but rather to using these advanced generative pre-trained architectures for analytical and classification purposes.