[avibhu]

Tangential: you can finetune something like flan-ul2 to do quote extraction using examples generated from chatgpt. If you have a good enough GPU, it should help cut down costs significantly

[specproc]

Nice, that sounds like it's worth exploring. Much appreciated.

Again though, it's the zero-effort part that's appealing. I'm on a very small team and getting that to close to the same standard will take time for a ham-fisted clod like myself. Worth giving a shot all the same though, thanks again.

[leobg]

The zero shot ability is convenient. But for tasks that you need to get done millions of times, I’d much rather spend $10 on GPU compute and maybe a day of training data generation to train a T5 which I then “own”.

Also, running your own specialized model locally can be much faster than using someone’s API.

[pfdietz]

It's interesting what you can do with ChatGPT with few shot learning. It generalizes at the drop of a hat, often correctly.

[winddude]

Don't they have in the ToS you aren't allowed to use outputs for training downstream? Which is a little ridiculous, considering it's ToS.

But yea, they cheap cost and lack of training is making me a take a long hard look at how I'm implementing more traditional NLP solutions.

[swyx]

> Don't they have in the ToS you aren't allowed to use outputs for training downstream?

you mean this? "Data submitted through the API is no longer used for service improvements (including model training) unless the organization opts in" https://openai.com/blog/introducing-chatgpt-and-whisper-apis

[hooande]

I do this. It works.

[icelancer]

Can you elaborate? Did some brief Google searching but had issues putting it together. We have thousands of documents and data stores we'd like to parse using GPT-3.5 (or the new ChatGPT API) and have been thinking of pretraining to cut things down. Thank you!