[deleted]

[deleted]

I didnt get paywall

Substack sometimes throws a pop-up that looks like a paywall, because they want your email, but it has a subtle link saying "Let me read it first".

maybe you hit that?

A model that takes actions to minimize uncertainty will appear to be curious. Intelligent sampling of the input space is the way to go.

> [this year], large language models (LLMs) finally got good.

Every year it's like dejavu with this shit since 2018.

"Wow, this test accuracy is way better!" "Ok, how does it do on OOD data?" "Hmm, not great. Let's train a bigger model."

"Wow, this test accuracy is way better!" "Ok, how does it do on OOD data?" "Hmm, not great. Let's..."

Can anyone send me the main papers describing the instructed and action driven models pleaseee ?

true, they do keep getting gooder and people are like "we solved it this year"

i think it got good enough for most things in 2020 with gpt-3 like how dall-e/SD is good enough for most things now

Is there an available read about document, concern the above..

I will appreciate if someone can help me.

The ubiquity of pretrained BERT + ResNet models in commercial software applications (and the measurable lift they deliver) is proof that they've been "good enough" for years. Sometimes these articles can come off a bit naive to the impact that the technology has already had or how widely it is used beyond the specific application that is most observable / accessible to the author.

How does this shit get up voted? There's a very clear constant improvement. The models are objectively better each year.

Because LLMs are incrementally better each year, not fundamentally better. The claim that now they are useful has become a cliche within the field of ML.

But they are useful. Look at the thousands of real world uses. Look at grammerly, translation, protein folding, and so on. How can you possibly deny it?!

> not fundamentally better

In just the last two years, the models went from scoring 43 on this system of testing to 75. How much more of a fundamental improvement are you after?!

You and I are literally saying the same things. These models have been in prod on every major software platform since BERT.

We don't even need to look at offline eval metrics anymore. If you're an actual MLE / data scientist you likely have the pipelines set up which directly measure the engagement / attributable sales differences and report the real business impact across millions of users each time a new model is released.

I work on a team that has made millions of dollars building applications on top of LLMs since 2018, so when I see the claim "LLMs finally got good this year" it's hard not to laugh. --this is what I am getting at.

Edit*: did you read the article?