11–20 of 94 posts
RAG is more oriented to temporary and variable situations.
In addition, LoRA is also a fine-tuning technology,and it is written in their paper.
Also the basic premise that knowledge injection is a bad use-case seems flawed? There are countless open models released by Google that completely fly in the face of this. Medgemma is just Gemma 3 4b fine-tuned on a ton of medical datasets, and it’s measurably better than stock Gemma within the medical domain. Maybe it lost some ability to answer trivia about Minecraft in the process, but isn’t that kinda implied by “fine-tuning” something? Your making it purpose built for a specific domain.
Wasn't there that thing about how large LLM's are essentially compression algorithms ( https://arxiv.org/pdf/2309.10668 )? Maybe that's where this article is coming from, is the idea that finetuning "adds" data to the set of data that compresses well. But that indeed doesn't work unless you mix in the finetuning data with the original training corpus of the base model. I think the article is wrong though in saying it…
"SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT ..."
I think of it as trying to encourage the LLM to want to give answers from a particular part of the phase space. You can do it by fine tuning it to be more likely to return values from there, or you can prompt it to get into that part of the phase space. Either works, but fiddling around with prompts doesn't require all that much MLops or compute power. That said, fine tuning small models because you have to power thr…
> That said, fine tuning small models Mostly referred to as model distillation, but I give the author the benefit of the doubt that they didn't mean that.
This is a pretty awful take. Everyone understands they are modifying the weights - that is the point. It’s not like these models were released with all of the weights perfectly accounted for and changing them in any way ruins them. The awesome thing about fine-tuning is that the weights are malleable and you have a great base to start from. Also the basic premise that knowledge injection is a bad use-case seems flawe…
He's proposing alternatives he thinks are superior. He might well be right too, although I don't have a horse in the race but LORA seem like a more satisfying approach to get a result than retraining the model and giving LLMs tools seems to be proving more effective too.
It's pretty frustrating to spend weeks on finetuning and end up with a model that says: "SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT SELECT ..."
I've hit this with gemini-2.0-flash and changing the prompt ever so slightly seems to make things work, just to break it at other input.
Fitting a lora changes potentially useful information the same way that fine-tuning the whole model does. It's just the lora restricts the expressiveness of the weight update so that is compactly encoded.
"Fine-tuning large language models (LLMs) is frequently sold as a quick, powerful method for injecting new knowledge" Is that true though? I don't think I've seen a vendor selling that as a benefit of fine-tuning.