LLM Lecture: A Deep Dive into Transformers, Prompts, and Human Feedback

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📺📺This video is an educational and historical deep dive into LLM research, where we would like to show you and convince you that while ChatGPT seemingly emerged overnight, it’s built on years of foundational work and innovation on LLM technology.

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Outline:
00:00 Lecture contents
01:30 Skillshare (Sponsor)
02:54 The Transformer
05:49 Tokenization
10:11 The Transformer layer
14:04 Attention
22:31 Position embeddings
27:42 Residual connections
29:55 How transformers learn language
34:40 Training Decoders (e.g., GPT)
40:46 Decoder inference / test time
43:55 Encoders (e.g., BERT)
47:45 Encoder-Decoders (e.g., T5).
53:24 Why LLMs need prompting
57:00 Vanilla prompting
01:01:35 Prompt tuning
01:03:03 In context (few-shot) learning
01:07:50 Chain-of-Thought
01:10:25 Retrieval Augmented Generation (RAG).
01:12:14 Beyond pre-training: Post-training techniques
01:13:50 Instruction Tuning
01:16:00 Preference Tuning with Human Feedback via DPO
01:19:20 RLHF key idea
01:21:14 Benchmarking LLMs: Emergent capabilities and how (not) to measure them
01:24:50 Multimodal extensions of LLMs
01:28:04 Gopnik’s Parable of Stone Soup AI

📑Allison Gopnik's Stone Soup AI: https://simons.berkeley.edu/news/ston...
📑Alexei Efros’s talk at the ‪@LaureateForum‬ :    • Lecture: Efros | September 23  

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Video editing: Nils Trost