<ul><li>LLMs go through pre-training and post-training phases to learn how language works.</li><li>Pre-training involves gathering diverse datasets like Common Crawl and tokenization.</li><li>Tokenization converts text into numerical tokens, essential for neural network processing.</li><li>Neural networks predict the next token based on context, adjusting parameters through backpropagation.</li><li>Post-training fine-tunes LLMs on specialized datasets to improve performance.</li><li>Inference evaluates model learning by predicting next tokens based on training.</li><li>Hallucinations occur when LLMs predict statistically likely but incorrect information.</li><li>Improving factual accuracy requires training models to recognize knowledge gaps.</li><li>Self-interrogation and fine-tuning help LLMs handle uncertainties in responses.</li><li>LLMs can access external search tools to extend knowledge beyond training data.</li></ul>

How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference

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