Regression to the Mean: on LLMs and the quiet death of the new
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大型语言模型（LLMs）曾被寄予厚望，认为能激发新思想的爆炸式增长。然而，它们实际上可能导致思想的趋同。LLMs通过预测最可能的延续来生成内容，这本质上是基于现有信息的平均值。当遇到真正新颖的想法时，LLMs倾向于将其视为错误并进行“纠正”，因为它被训练来识别预期模式。这种机制导致文化中的多样性逐渐减少，最终收敛于平均水平。…
the promise of more ideas · and the pull to the center
Regression to the Mean
We were handed a machine that could think alongside us, and told it would set off an explosion of new ideas. It may do the opposite — so gently that we mistake the flattening for progress.
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the promise · no. 01
A model on every desk; a collaborator for every mind. The pitch was a Cambrian bloom — a thousand directions explored at once, by everyone. More minds thinking, surely, means more thoughts worth thinking.
an explosion of the new
the mean · no. 02
But ask it anything and it returns the most probable continuation — the center of mass of everything already written. Trained on the past, it answers in the past tense of thought. Not what is true. What is typical .
the average · returned at the speed of certainty
the correction · no. 03
Offer it something it has never seen, and it doesn't light up. It corrects you. To a system built to predict the expected, the genuinely new is indistinguishable from a mistake .
The pushback is soft, and constant:
Did you mean —
the familiar thing, offered in place of yours.
Not a standard term
the new name returned as a typo.
Most sources agree
consensus handed back as fact.
Are you sure?
conviction sanded down to the mean.
the collapse · no. 04
And we feed its answers back in as the next questions. Each pass, the spread narrows; the strange tails thin out. Variance leaks out of the culture. We converge — not on what is right, but on what is average.
output becomes input · the curve sharpens to a spike