[nl]

> We've come to the consensus that large language models are just stochastic parrots

Anyone who thinks this REALLY doesn't know how language models work. A properly trained LM will only parrot something back because of lack of diversity in training data. This does happen in some cases (eg, GPL license or something) but those are pretty unique cases.

People on HN seem to think this a lot, but they are just wrong.

[chaxor]

It'sespecially true for ML in general on HN, but it's generally true for a lot of areas in the public - people often mistake skepticism for expertise or knowledge. I think the phenomenon is similar to the large crowd that cries "the sample is too small" any time statistics are brought up.

It's the first thing anyone learns, and it's easy to do.

It's really unfortunate, but that's why you see so many on HN that dismiss new technologies in ML (especially in NLP, since everyone can understand the output - that's less true in e.g. protein folding)

[nl]

> people often mistake skepticism for expertise or knowledge

This is a pretty good insight.

> that's why you see so many on HN that dismiss new technologies in ML (especially in NLP, since everyone can understand the output

I think also in NLP people see output that is the same as some training data, so think it is copying it. It takes some a little bit more thought to think "ok if I asked 100 experts to try to write how to sort an array in Python" or their code is going to be very similar. This doesn't mean it is copied.