AccountingBench: Evaluating LLMs on real long-horizon business tasks

I guess having access to tools / running Python would make all the difference.

This is a task where access to Python would be immensely helpful, yes? Interesting that there's not much of a difference between the "analytical" LLMs with tool use and ones that do not (...assuming o3 etc did get to use python?).

I think the first chart could be a beautiful summary of what's driving LLMs into a bubble. At first, they're amazing and will obviously be able to improve productivity if not replace employees outright: C suites and venture capitalists around the world rejoice and begin pumping in billions of dollars of investments. But as time goes on, the demands placed on actual human employees become clear. Far from being able to replace an employee, the employee using the LLM might spend more time cleaning up its messes than had they done it themself.

Yes, LLMs have and will continue to improve. But it's that initial "holy shit, this thing is basically as good as a real accountant" without any understanding that it can't sustain it which leaves many with an overinflated view of their current value.

Remember that test where you ask a LLM whether 9.11 or 9.9 is the bigger number? [Just checked gpt-4o still gets it wrong]

I don't think you'll find many sane CFOs willing to send the resulting numbers to the IRS based on that. That's just asking to get nailed for tax fraud.

It is coming for the very bottom end of bookkeeping work quite soon though, especially for first draft. There are a lot of people doing stuff like expense classification. And if you give an LLM an invoice it can likely figure out whether it's stationary or rent with high accuracy. OCR and text classification is easier for LLMs than numbers. Things like concur can basically do this already.