ChatGPT - Explained!
Channel: CodeEmporium
Date: 2022-12-12

We are going to talk about the internal workings of ChatGPT and the fundamental concepts it lies on: Language Models, Transformer Neural Networks, GPT models and Reinforcement Learning

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Transformer Neural Networks: https://www.youtube.com/watch?v=TQQlZhbC5ps

RESOURCES
[1] ChatGPT blog: https://openai.com/blog/chatgpt/ 
[2] Instruct GPT which is the model ChatGPT was modeled after: https://arxiv.org/pdf/2203.02155.pdf
[3] Proximal Policy Optimization is how ChatGPT makes use of human rankings to update model parameters and make it more "safe" and "truthful": https://openai.com/blog/openai-baselines-ppo/
[4] Here is a paper that shows how Reinforcement learning through human feedback actually helps:  https://arxiv.org/pdf/2009.01325.pdf
[5] Every timestep, a subword token is generated. Here is some more information on this process with BPE: https://towardsdatascience.com/byte-pair-encoding-subword-based-tokenization-algorithm-77828a70bee0
[6] Basic Concepts in Reinforcement Learning:  https://www.baeldung.com/cs/ml-policy-reinforcement-learning
[7] Why Does GPT-3 write non-sensical stuff that sounds legit? https://www.alignmentforum.org/posts/BgoKdAzogxmgkuuAt/behavior-cloning-is-miscalibrated
[8] What is GPT-3.5? https://beta.openai.com/docs/model-index-for-researchers



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