How LLMs, like ChatGPT, Learn

ML Community: www.gptandchill.ai/machine-le...
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Large Language Models (LLMs) are trained through a process that leverages vast amounts of text data sourced from the internet. This data is preprocessed to extract linguistic patterns and relationships, which are then used to train neural networks with architectures such as transformer models. During training, the model iteratively adjusts millions of parameters using techniques like backpropagation and gradient descent to minimize prediction errors. LLMs employ attention mechanisms to prioritize relevant words and phrases within contexts, enabling them to generate coherent and contextually appropriate text. Fine-tuning further enhances their performance on specific tasks by adapting pretrained models to domain-specific datasets. The training process involves balancing computational resources and dataset diversity to optimize for both efficiency and effectiveness in language understanding and generation tasks.

[@gptLearningHub]

Small Clarification: Most LLMs actually process inputs and outputs on a sub-word level instead of a strict word level split, but thinking about this in terms of words can help simplify things at first!

[@avi12]

Your videos are great, but because I don't really come from a background in ML or neural networks, even though I feel like I understand the concept you're teaching, I don't actually understand why the code solution works

[@vedantbhardwaj3277]

Share problem link

[@gptLearningHub]

You can try the problem here neetcode.io/problems/gpt-dataset or see the full list here www.gptandchill.ai/codingproblems

[@vedantbhardwaj3277]

@@gptLearningHub thanks