How LLMs work, clearly explained with visuals: pic.twitter.com/5JuJ5A3tEB
— Sumanth (@Sumanth_077) 25 mai 2024
How LLMs work, clearly explained with visuals:
LLMS
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How LLMs Work: Clear Visual Explanation
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AI Quality Debate: Google vs Competition and Future Improvements
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They're all like LOL look how dumb AI is I agree btw if it's anything Google makes, it's shit But most other stuff is great already and only getting better Blindspot is "it's not perfect for my super edge case so it'll never be" Nah it will, 1-5 years
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Sharing LLM OS Blog Post at AI Engineering Singapore Event
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had a fun time mouthblogging my LLM OS blogpost I've been cooking on for the past month 🙂 AI Eng Singapore is alive and well thanks to Gabriel and @ivanleomk and all the other tech scene friends! https://t.co/rB5rPbXoKb pic.twitter.com/qDUhX6ayEB
— swyx 🐣 (@swyx) 25 mai 2024had a fun time mouthblogging my LLM OS blogpost I've been cooking on for the past month 🙂 AI Eng Singapore is alive and well thanks to Gabriel and @ivanleomk and all the other tech scene friends!
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Showcase Proficiency Recent AI Technologies LLMs Generative
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Tip 6: Focus on Recent Technologies Mention your proficiency with LLMs, reinforcement learning, or other generative AI technologies. Highlight any recent work or projects involving these technologies.
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LLM Bugs: Finding and Discussing Detection Methods
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LLM bugs and how to find them Part of the joy of curating @aidotengineer is I get to bring some of the best people in the community that you only see online, to meet and teach in person. Several of our speakers are giving their first talks -ever-! Daniel is one of the most
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LLMs trained on specific Myers Briggs personality type text
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Random idea: LLMs trained on written words only from people of a specific Myers Briggs type.
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LLMs Understanding World vs N-gram Models: Insufficient Argument
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"LLMs understand the world because that's needed to predict the next word" is an insufficient argument: you could say the same about n-gram models.
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Team achieves breakthrough in simplified model customization
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Very proud of the team, a first step towards making model customisation much simpler.
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Seven Sins of Language Model Evaluation: What Makes Evals Successful
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New blog post where I discuss what makes an language model evaluation successful, and the "seven sins" that make hinder an eval from gaining traction in the community: https://
jasonwei.net/blog/evals Had fun presenting this at Stanford's NLP Seminar yesterday! -
Transformer blocks progressively refine information through attention mechanism
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(More likely though, each block refines the information over time in the Transformer forward pass, enriching it with the information gathered from previous tokens during Attention.)