LLMs process questions via tokenization, embeddings, and attention

Large language models like ChatGPT, Gemini, and Microsoft Copilot process user questions through a series of steps, beginning with tokenization and converting these tokens into numerical embeddings that represent their meaning. Positional encoding is added to maintain word order, followed by a self-attention mechanism that allows words to understand their context within the sentence. This process is enhanced by multi-head attention and feedforward neural networks, with multiple layers stacking to refine the model's understanding before it predicts a response token by token. The final output is then converted back into human-readable text. AI

IMPACT Explains the core mechanisms behind LLM question processing, including tokenization, embeddings, and attention, crucial for understanding AI agent behavior.

RANK_REASON The cluster describes the internal workings of LLMs and the process by which they understand and respond to user queries, akin to a technical paper or explanation.

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• Burp Suite
• ChatGPT
• Claude
• Gemini
• Microsoft Copilot
• OWASP LLM Top 10
• Perplexity
• Snyk
• OWASP ZAP
• Transformer architecture

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