Emergent can be defined as the sudden appearance of novel behavior.
In reality, if you ask a good LLM to solve a math-related problem, it will write and run a Python program to return the answer. Sometimes this even works. Sometimes it returns garbage. And sometimes it realizes that its own answer isn't realistic and tries a different approach. Sometimes people claim this isn't a valid manifestation of intelligence.
Completely pointless study, unworthy of Wired or HN.
Which is stupid, because it's not. A pocket calculator can perform arithmetic, as can a Python program, or for that matter a Japanese cormorant, who counts the fish it helps you catch. None of those are considered capable of "reasoning" on their own.
Meanwhile, GPT4 will cheerfully write a program to carry out the required arithmetic operations (and then some.) A study that doesn't acknowledge that is worthless at best.
We don't want to see if the LLM can be used as a tool to do arithmetics, but whether it can learn complex data relationships like arithmetics. Arithmetics is a stepping stone, not a goal so that the model can solve it by invoking a calculator isn't relevant. The problems we want it to solve doesn't have tools like calculators so it doesn't help getting us there.
Agreed that generalized architectures will ultimately win out over hand-tweaked ones. But the patent wars that will eventually be fought over this stuff are where the real bitter lessons will come into play. At some point, we'll be forced back into the hand-optimization business because someone like OpenAI (or another Microsoft proxy) will have locked down a lot of powerful basic techniques.
It was noticed that LLMs can do some arithmetic, but we are yet uncertain how much and how it happens exactly.