The Bitter Lesson is that human-written rule models will always lose out to trained models in the end.
Coding an exercise app will benefit from a model that can code and understands what exercise is. A car showroom website will benefit from a model that understands what relevant filters would be.
Nah, let’s stand firm here and not give in so easily. Parent suggests a coding model needs exercise info in case you want to code an exercise model? Absurd, that leads to the know everything monolith to do anything.
Language, reasoning, tool use, and domain knowledge.. in this case the domain is coding. Someone wants an exercise app? Great, so we look that shit up, we don’t bake it in. We don’t want to bake anything we don’t need in.
We don’t even need to explain this completely reasonable and totally normal engineering preference that small is beautiful. it’s the other side that needs to justify wtf exercise has to do with coding or why everything from twitter posts and a working knowledge of 15th century French poetry might be helpful for serious work in contemporary astrophysics
No, the point is having broader knowledge than just "can code" is important for building software for humans to use.
> We don’t even need to explain this completely reasonable and totally normal engineering preference that small is beautiful.
It is, of course it is, as long as that small thing is useful. My argument is not "let's put literally everything in this thing just in case" it's "broader world knowledge is important for building many things".
Also, we don't look at CVs or histories of people and say "hmm, this person could be good at coding for us but I really wish they didn't have such a broad and expansive history".
> Great, so we look that shit up
But what do you have it look up? What is exercise? What is a muscle and what is a human? How is it going to understand those references? Does it have the broad enough understanding to see where the gaps are?
At some level unless all you want is "loop over the array and sum the results into a variable called sales_total" converted into code, you have to have some broader knowledge about things.
And once you start considering that maybe it needs more than simply code, we're not disagreeing we're just discussing where the line should be. Which I've also not placed, because there's no simple statement to make on that.
I would assume a smaller model trained specifically to produce code based on natural language input would perform at least just as well, which would already be better given the smaller size / probably lower training resource requirements. This is of course, as already said, an assumption. But generalizing typically comes at a cost, there is no reason why this wouldn't apply to LLMs, it even applies to humans.