Let's explore how Large Language Models (LLMs) like ChatGPT, Claude, Gemini generate text, focusing on decoding strategies that introduce randomness to produce human-like responses. We break down key sampling algorithms such as top-k sampling, top-p sampling (nucleus sampling), and temperature sampling. Additionally, we dive into an alternative method for text generation, typical sampling, based on information theory. References: [1] Locally Typical Sampling, by Clara Meister et al: https://arxiv.org/pdf/2202.00666 Video sections: 00:00 How LLMs generate text (Overview) 00:56 Why Randomness in text generation? 02:12 Top-k 03:22 Top-p 04:44 Temperature 06:04 Entropy and Information Content 07:12 Typical Sampling ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: https://www.assemblyai.com 🐦 Twitter: https://twitter.com/AssemblyAI 🦾 Discord: https://discord.gg/Cd8MyVJAXd ▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1 🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers 🔑 Get your AssemblyAI API key here: https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_marco_2 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning