> but you can’t trust them to do a calculation in the middle of a task.
You can't trust a person either. Calculating is its own mode of thinking; if you don't pause and context switch, you're going to get it wrong. Same is the case with LLMs.
Tool usage and reasoning and "agentic approach" are all in part ways for allowing LLM to do the context switch required, instead of taking the match challenge as it goes and blowing it.
The proper comparison is not a human, it’s a computer. Or even a human with a computer.
But my point wasn’t to judge LLMs on their (in)ability to do math - I was only responding to the parent comment’s assertion that they’ve gotten better in this area.
It’s worth noting that all of the major models still randomly decide to ignore schemas and tool calls, so even that is not a guarantee.
You can't trust a person either. Calculating is its own mode of thinking; if you don't pause and context switch, you're going to get it wrong. Same is the case with LLMs.
Tool usage and reasoning and "agentic approach" are all in part ways for allowing LLM to do the context switch required, instead of taking the match challenge as it goes and blowing it.