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Thoughts on Inference-time Compute
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A. Pre-training vs. Inference-Time Compute
Abilities of LLMs and Information Density in Language
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Idea of Information Density
This snippet is from an Nvidia fireside chat with Ilya Sutskever and Jensen Huang (38:30~).
OpenAI o1 & Recent Trend of LLMs
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OpenAI’s o1 announcement might just be the event that triggered the most dopamine for me recently. Over the past two days, I stayed up late into the night researching it and tried playing around with it myself. It might sound like somewhat hyperbolic, but I can’t help but feel that I am witnessing one of the most pivotal moments in human history. With both excitement and fear, I’ll take a moment to organize my thoughts about o1.
From Structured to Unstructured - A Quatitative Breakthrough of Data
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A single technology can hold vastly different meanings depending on one’s perspective. The more you know—and the more imagination and insight you possess—the deeper that meaning becomes. The more I observe LLMs, the more this thought resonates. Some dismiss LLMs as limited because they simply predict the next token autoregressively. But I disagree. The potential of a machine that has mastered language is immense and will advance much further, depending on how much imagination we’re willing to apply.
LLM and Wittgenstein’s picture theory
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These are some unconcluded thoughts that seem worth writing down.