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199 posts · best match first · index holds 133,416 posts · last 36 hours · 0.67 s

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  3. @askfred.beBluesky

    Every production LLM relies on a tokenizer to chop text into chunks, hiding raw bytes to keep context manageable. Feeding raw UTF-8 bytes directly into standard Transformers actually beats subword models once parameter scale compensates for 4x sequence lengths. Tokenization was a compute crutch.

    arxiv.org Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation -

    Has the hashtagAI
    Read post · Bluesky
  4. @harkov311.bsky.socialBluesky

    Again, some people apparently need reminders that LLMs are simulators. They simulate human responses because human inputs are what the prediction models are trained on. Like, we all understand that the ray tracing in Doom Eternal doesn't mean your GPU has actual light sources inside it

    Has the hashtagAI
    Read post · Bluesky
  5. @shibbi.meBluesky

    Y’all mock Yud for tryna get people to think more logically about AI And admittedly the result was largely the invention of new ways to be profoundly illogical about AI But you gotta compare against the baseline here

    Has the hashtagAI
    Read post · Bluesky

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