LLM training explained: Backpropagation and self-supervised learning

Large language models like Llama 3.1 8B are trained through a process where the model predicts the next token in a text sequence and then adjusts its internal numbers, or weights, based on the error in its prediction. This adjustment is guided by backpropagation, a calculus technique that calculates how much each weight contributed to the error. The process, rooted in concepts dating back to the 1970s and 1980s, involves billions of weights and is iterated over vast amounts of text, as exemplified by the training of GPT-3. AI

IMPACT Explains the core mechanics of LLM training, detailing how models learn from vast text datasets via backpropagation and self-supervised learning.

RANK_REASON The item explains the technical process of LLM training, focusing on concepts like backpropagation and self-supervised learning, referencing historical papers and figures. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• backpropagation
• Geoffrey Hinton
• GPT-3
• Hinton
• Linnainmaa
• Llama-3.1:8b
• Meta
• Nature
• Paul Werbos
• Rumelhart
• self-supervised learning
• Williams Racing

• paper
• infra

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