LoRAs or even full fine-tunes would be much cheaper than that, and with some investment in the right infra could be updated regularly. And at least LoRAs can be swapped in and out cheaply, making them usable in large-scale inference providers. But there seems to be limited appetite in offering this. Both Anthropic and OpenAI no longer offer fine tuning for current models
There was a recent-ish paper[0] from Sakana AI about baking facts from a document corpus into a LoRA adapter. Claimed near perfect recall on very large needle-in-haystack testing. Haven’t tried it myself though.
Is this because of catastrophic forgetting, or is that more or less solved?
Is it, alternatively, because loading your LoRA plus someone else's can't be done in the same batch, so inference is made harder or becomes impossible to do at scale?
It is about context window too so no matter how much money you put to train your model it can still have degraded performance in following instructions but it will be better to execute the instructions they can follow.
It is also about the harness and how that can help drive the model and pick and choose what to include or not in the context.
These LLMs do not understand the project. For them any next word is good as long as it was picked by the token predictor. It does not have any way to understand but only to do. probability distribution over their vocabulary and if that vocabulary is tainted and lost parts of the original context what is a good candidate there will not match the intention of the initial project.