How Language Models Learn to Tell True from False
Big language programs can quietly pick out truth inside their brain, and you might wonder how that happens.
New simple experiments show a toy model can build clear internal lines that split true from false, even with tiny rules.
The trick seems to be that true facts often appear next to other true facts, so the model learns to use that pattern to guess what comes next.
Over time it forms what looks like linear truth directions — a kind of straight line inside its math that points to truth.
Learning moves in two steps: first the model memorizes some fact links fast, then slowly it shapes those links into a neat, general split — we call that two-phase learning.
Tests on bigger, real models show similar behavior, so this is not just toy story, its a clue to why models often sound confident when they are right, and sometimes when wrong.
This idea about co-occurrence of facts helps explain how models get better answers, and why they still make surprising mistakes.
Read article comprehensive review in Paperium.net:
Emergence of Linear Truth Encodings in Language Models
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