[mountainriver]

It's wild to me how many people still think that fine-tuning doesn't work. There was just a thread the other day with numerous people arguing that RL should only be done by the big labs.

There is so much research that shows you can beat frontier models with very little investment. It's confusing that the industry at large hasn't caught up with that

[jokethrowaway]

the subject of this news likely doesn't generalise to random documents

You need some serious resources to do this properly, think about granite docling model by IBM.

For LLM: Finetuning makes sense for light style adjustments with large models (eg. customize a chat assistant to sound a certain way) or to teach some simple transformations (eg. a new output format). You get away with 100-1000 samples.

If you want to teach new behaviour you need a lot of data, likely too much to justify the investment for your average chatgpt wrapper AI company. The pragmatic choice is often to just prompt engineer and maybe split your task and combine multiple prompts

[mountainriver]

The pragmatic case is always prompt engineering. I’m speaking to the common idea that fine tuning doesn’t work, which if you need it, and have the capital (which isn’t all that much) it’s helpful

[selim-now]

interesting, I would argue that fine-tuning makes sense especially in cases where you want to narrow down a small model to a single task – in this case you can get the most bang-per-parameter in a way, using a small model that performs very well in a very narrow space.