In this episode of Nate’s Notebook, I asked the AI to dive into latent space and large language models (LLMs), exploring how we can prompt these models and what happens behind the scenes. Latent space is like a hidden map where data is organized, allowing AI to make sense of complex patterns, like grouping similar concepts together. I’ll be summarizing a range of sources to explain how LLMs, like GPT, use latent space to understand our prompts and generate responses. It’s all about how these models “compress” knowledge and respond based on the structure they’ve learned from data patterns. Let’s break it down!
Sources:
https://arxiv.org/html/2402.14433v1
https://training.continuumlabs.ai/disruption/search/latent-space-versus-embedding-space
https://www.hopsworks.ai/dictionary/latent-space
https://www.fewshotlearning.co/p/thinking-about-latent-space
https://arxiv.org/pdf/2304.09960
https://openreview.net/pdf?id=4y3GDTFv70
https://www.youtube.com/watch?v=N8p6u1OtARs
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