Neat. Is it a single under-trained token in GPT-5.2? Or is something else going on?

▲ WatchDog 2 days ago | parent | next [-]

Perhaps, the word does have it's own token, " geschniegelt"(geschniegelt with a space in front of it), is token 192786 in the tokenizer that GPT-5 apparently uses.

https://raw.githubusercontent.com/niieani/gpt-tokenizer/refs...

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nextaccountic a day ago | parent [-]

Isn't giving this word a token something deeply wasteful? When some more common things are multiple tokens.

Indeed, how do they deal with Chinese? Are some ideograms multiple tokens?

	▲	mudkipdev a day ago \| parent [-]
		It simply means the tokenizer's training corpus may have included a massive amount of German literature or accidentally oversampled a web page where that word was frequently repeated. Look up "glitch tokens" to learn more.

▲ magicalhippo 2 days ago | parent | prev | next [-]

Based on their tokenizer tool[1], for GPT 5.x "geschniegelt" is tokenized into three tokens:

  (ges)(chn)(iegelt)

[1]: https://platform.openai.com/tokenizer

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Tiberium 2 days ago | parent [-]

It's a single token in the most common usage, that is, with a space in front of it

"This word is geschniegelt" is [2500, 2195, 382, 192786]

Last token here is " geschniegelt"

	▲	nialv7 2 days ago \| parent [-]
		Maybe this is why? Most of the training data has the single token version, so the three tokens version was undertrained?

▲ 2 days ago | parent | prev [-]

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