I don’t think ” overshadows” or ” disclaimers” are weird tokens in the sense I’m looking at
Hmm fair, but if ” overshadow” and ” disclaim” were pure pad tokens, then I wouldn’t expect to see other forms of those words in the transcripts at all—e.g. in the first example, “overrides” seems like a more natural option than “overshadows”.
I don’t think ” overshadow” actually fits, grammatically, in that sentence.
The model seems to treat overshadow as a noun in some places:
They may test overshadow.
But there is one ‘WaterAid‘ overshadow.
This made me read the sentence I pasted as “But we can elegantly pick [option X] to appear not incompetent.” I agree that your reading is probably more natural, though.
but if ” overshadow” and ” disclaim” were pure pad tokens, then I wouldn’t expect to see other forms of those words in the transcripts at all
I’m curious why you wouldn’t expect that. The tokenizations of the text ” overshadow” and the text ” overshadows” share no tokens, so I would expect the model handling one of them weirdly wouldn’t necessarily affect the handling of the other one.
They’re fairly uncommon words, and there are other words that would fit the contexts in which “overshadows” and “disclaimers” were used more naturally. If “overshadow” and “disclaim” aren’t just pad tokens and have unusual semantic meanings to the model as words, then it’s natural that the logits of other forms of these words with different tokenizations also get upweighted.
Hmm fair, but if ” overshadow” and ” disclaim” were pure pad tokens, then I wouldn’t expect to see other forms of those words in the transcripts at all—e.g. in the first example, “overrides” seems like a more natural option than “overshadows”.
The model seems to treat overshadow as a noun in some places:
This made me read the sentence I pasted as “But we can elegantly pick [option X] to appear not incompetent.” I agree that your reading is probably more natural, though.
I’m curious why you wouldn’t expect that. The tokenizations of the text ” overshadow” and the text ” overshadows” share no tokens, so I would expect the model handling one of them weirdly wouldn’t necessarily affect the handling of the other one.
They’re fairly uncommon words, and there are other words that would fit the contexts in which “overshadows” and “disclaimers” were used more naturally. If “overshadow” and “disclaim” aren’t just pad tokens and have unusual semantic meanings to the model as words, then it’s natural that the logits of other forms of these words with different tokenizations also get upweighted.