AI Knowhow How LLMs Actually Work
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Dec 2, 2024 Dive into the fascinating world of large language models (LLMs) and discover how they predict words and synthesize complex information. Explore the mechanics behind neural networks and attention mechanisms that power these AI marvels. Learn about the shift from fine-tuning to retrieval-augmented generation, which enhances model efficiency and response accuracy. Finally, uncover how understanding biases and hallucinations in LLMs can improve organizational communication and adaptability in a rapidly evolving AI landscape.
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LLMs as Next Word Predictors
- LLMs predict the next word or token in a sequence, similar to a fill-in-the-blank generator.
- This prediction happens recursively, with each predicted token becoming the input for the next prediction.
LLMs and Numerical Prediction
- LLMs tokenize words, converting them into numbers to predict the next numbers in the sequence.
- This process, based on complex mathematical relationships, allows LLMs to generate human-like text.
LLM Training and Data Processing
- LLMs learn by processing vast amounts of text data, identifying patterns, and predicting the next word.
- This 'training' allows LLMs to understand word relationships and generate meaningful text, even without explicit grammar rules.