How Tokenization Affects What Language Models Can Learn By Aiko Murakami Before any text ever reaches a language model’s inner workings, it gets quietly, thoroughly broken down. This step is called tokenization—a kind of pre-reading that chops a running stream of characters into bite-sized chunks called tokens. The model never encounters words as we do. …
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Where the First Cuts Fall: How Tokenization Shapes What Models Grasp
I type a sentence. Before it ever meets the larger machinery, a quiet process tears it apart. Not into words, exactly—more like fragments. The string “unhappy” might stay whole, or it might become “un” and “happy.” Each piece gets a number. That’s tokenization. It sounds like clerical work, the sort of thing you’d relegate to …
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The Hidden Architecture of Words: How Tokenization Shapes Language Model Learning
When I read a sentence, the words flow together as if they were always meant to be that way â each one a tidy packet of meaning. But the moment a machine touches that same sentence, it gets carved up. Sometimes into whole words, sometimes into slivers like âun-happi-ness,â and occasionally down to single letters. …
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The Philosophy of Meaning and Why It Matters for AI
When we ask what something means, we rarely settle for a dictionary definition. Human meaning is always something more—an interplay of intention, context, and the entire life of the person asking. Aiko Murakami has spent years watching technical systems stumble over exactly this layer of significance, and the hitch isn’t just about data or computing …
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Why Large Language Models Do Not Understand What They Say
When a text-generation system produces the sentence “The cat sat on the mat,” it has performed a remarkable statistical feat. It has selected each word from thousands of candidates, arranged them according to patterns absorbed from billions of text samples, and delivered a grammatically correct string. What it has not done is form any conception …
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