Think back to the last long novel you read. By the time you reached the final chapter, you probably weren’t holding every comma or adverb in your head. You remembered the broad strokes—key themes, a few characters’ defining moments, a twist that reframed everything. Information-processing systems work under a similar constraint: they have a fixed …
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The Problem With Evaluating Language Generation Quality
When we come across a piece of generated text, our initial reflex is to judge it. Does it sound natural? Does it hold together? Does it say what it’s supposed to say? On the surface, these questions look straightforward, but underneath they hide a tangle of theoretical and practical problems. Judging the quality of language …
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Why Semantic Drift in Language Models Is Hard to Measure
Hand a sentence to a system built on mountains of text, and you expect meaning to stay put. Words ground us—or so we like to think. But the meanings that surface can warp quietly, sometimes radically. That warping—semantic drift—isn’t just a headache for engineers. It unsettles anyone watching language buckle under computational weight. I’ve spent …
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How Tokenization Affects What Language Models Can Learn
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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