Deep Dive into LLMs like ChatGPT by Andrej Karpathy | In-depth Analysis of Large Language Models (like ChatGPT): Full Training Process, Underlying Principles, Defects, and Future Development
Deep Analysis of Large Language Models (such as ChatGPT): Training Process, Underlying Principles, Defects, and Future Development I. Popular and Easy-to-Understand Overall Interpretation (Analogy + Key Comparisons) This article is a popular science video script for the general public. It completely disassembles the entire process of a large model from 0 to usable. A simple analogy can be made using a student's schooling process: Pre-training = Massive Extensive Reading of Extracurricular Books: Cleaning and processing massive amounts of webpage text from the internet and feeding it into a neural network. It's like a student reading all the articles, encyclopedias, and news on the entire web, not doing exercises, but only learning the writing patterns of language and text, and world knowledge. The output is called a Base Model, which can only automatically write text and not act as a chat assistant. Analogy: Base Model ≈ a machine that has read a massive amount of information from the entire web, but no one has taught it how to answer questions. If you input half a sentence, it will only continue to write webpage-style text and won't understand your question. Supervised Fine-Tuning SFT = Doing Example Problems with Standard Answers…
A YouTube video provides an in-depth analysis of large language models, such as ChatGPT. The video explains the entire process of building a large model from scratch, using an analogy of a student's education to illustrate the process. The model is trained in several stages, including pre-training, supervised fine-tuning, and reinforcement learning.
The video also discusses the limitations and defects of large models, including their tendency to "hallucinate" or generate false information, and their reliance on tokenized text rather than raw characters. Additionally, the video touches on the future development of large models, including the integration of multi-modal capabilities and AI intelligent agents.
The video is aimed at a general audience and provides a comprehensive overview of large language models.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.
