ChatGPT - Explained!

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-
[6] Basic Concepts in Reinforcement Learning: https://www.baeldung.com/cs/ml....-policy-reinforcemen
[7] Why Does GPT-3 write non-sensical stuff that sounds legit? https://www.alignmentforum.org..../posts/BgoKdAzogxmgk
[8] What is GPT-3.5? https://beta.openai.com/docs/m....odel-index-for-resea



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