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This post explores the intersection of large language models and information theory by using token surprisal to visualize semantic density within text. It demonstrates a practical application of ML metrics for enhancing readability, though it acknowledges that this visualization method may be distracting to some readers. The core focus remains on how predictive modeling can reveal structural properties of language rather than discussing consumer products or general software updates.

Sources coveringLessWrong, SiliconANGLE

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Using LLM surprisal to highlight informative words

LessWrongOctober 9, 20261 min read

Words harder to predict from context usually carry more information. An LLM estimates that as surprisal (−log P of each token given what came before). Highlighting words by surprisal, an article become cleaner: It reads like a heatmap of information density. For me and some other people it helps a lot while reading; others find it more distracting. At least, it’s an interesting spot where LLMs, linguistics, and information theory meet. Here are more pics: Discuss

It reads like a heatmap of information density. For me and some other people it helps a lot while reading; others find it more distracting. At least, it’s an interesting spot where LLMs, linguistics, and information theory meet.

Full text under the publisher's open licence · 71 words

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