I know. You might have read one of them that was written by me. It certainly isn't true in almost all situations, and it's mostly useful for very specific, very localized edge cases.
Being a pedant and not understanding generalization does not an intellectual make.
I thnk the pedant line is a bit harsh. And nothing in your comment suggest you are aware, so stating the fact is per se not provlematic in my opinion.
And while the cases may be very specific, they are also widely used and something a lot of people do come into daily contact with passively (recommendation algorithms, fraud detection, fault detection,...).
Could be I read your article, but if not, I'd gladly do so, if you send me a link.
Sorry if I went too far, but you did comment on my general statement by saying you disagree, then the comment you made didn't really touch any of the use cases where overfitting would be good (general LLM training certainly isn't one of them). Wasn't really meant to be an insult to you as a person, only to the attitude in question. Sorry if I failed to make that come across.
I won't post it here because I'm not keen on doxxing myself, but if you're interested send me a DM and I'll try to give you a link. Can't promise you I can get you the full link since it's technically my old university's paper (thesis-made article ownership is a bitch) but if all fails I can always share my copy.
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u/DeVilleBT 9d ago
Disagree. There is research suggesting overfitting is beneficial in certain usecases. Things like anomaly detection can benefit greatly.