People get mad when you call LLMs what they are. It will pass, as with all things.

Well, no, it isn't. You are looking for machine learning research. That list is only about LLMs, a very specific and over-hyped sub-sub-application of ML techniques.

If all you want is to attach yourself to the hype cycle, then that link still won't be enough, but at least it's a start.

You will find that in hype circles such as NLP there's a lot of thought-terminating cliches passed around by people who are not so deep in the weeds. Someone says something with confidence, another person doesn't know how to vet it and so just blindly passes it on, and all of a sudden a hack becomes a rumor becomes dogma. It seems to me to be this way with context vs memory.

Put another way: it's the kind of attitude that says "No, Mr. Ford, what we wanted was faster horses".

The trick with genetic algorithms is you have to tune your approach very specifically to the kinds of things you're modelling. Different animals mate and evolve differently, in the analogical view.

It's not enough to just do the textbook "1D chromosome" approach. You have to design your "chromosome", as well as your "crossover" and "mutation" operators specifically to your problem. In my experience, the crossover implementation is the most important one to focus on.

> Dex

For the last freakin time, LLMs are not the be-all end-all of machine learning...

What about ONNX? Most if not all feedforward models can be represented as ONNX.

Reinforcement learning does seem like a good option, but you'd need a bit of nuance in your reward function for training to work IMO. It would need to be more complex (and less sparse) than just "did you choose the correct next item or not".