When I ask a human to do 13 digit addition, 99.999% of them will do the addition in steps, and almost nobody will immediately blurt out an answer that is also correct without doing intermediate steps in their head. Addition requires carries, and we start from least to most significant and calculate with the carries. That is what 1-shot refers to.

If allow LLMs to do the same instead of producing the output in a single textual response, then they will do just fine according to the cited paper.

Average humans can do multiplication in 1 step for small numbers because they have memorized the tables. So can LLMs. Humans need multiple steps for addition, and so do LLMs.

Ok. In the context of AI, 1-shot generally means that the system was trained only on 1 example (or few examples).

Regarding of the number of steps it takes an LLM to get the right answer: isn't it more important that it gets the right answer, since LLMs are faster than humans anyway?

I am well aware what it means, and I used 1-shot for the same reason we humans say I gave it "a shot", meaning attempt.

LLMs get the right answer and do so faster than humans. The only real limitation here is the back and forth because of the chat interface and implementation. Ultimately, it all boils down to giving prompts that achieve the same thing as shown in the paper.

Furthermore, this is a weird boundary/goal-post humans get stuff wrong all the time, and we created tools to make our lives easier, if we let LLMs use tools, they do even better.