But can't it, literally, hallucinate raw data at any point in the run?
Alls LLM have this risk but somehow nobody seems to care or they think they can order the LLM to stop with a better prompt.
AccountingBench: Evaluating LLMs on real long-horizon business tasks
81–90 of 154 posts
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#82> In fact, we explicitly prompt against this behavior in no uncertain terms, but the instructions – and the entire spirit of the task – are lost in the interest of making forward progress LLMs and humans are quite alike. :) I notice that a few models will give up instead of ignoring their instructions and that's the model I would want working on tasks like this. An LLM should be able to categorize and reconcile trans…
> but if it's not sure, it should quit
Can it be sure or not? I've never been able to get LLMs to give confidence measures that match their actual outputs. I'll ask an LLM "Are you sure?" and it'll reply "Absolutely" when it's output is completely wrong, or it'll backtrack on a correct output with "I should not have provided an answer when I was unsure. Here is an answer I am sure of..." and then provide something completely wrong.
If they can't properly and consistently score their confidence, how do they "know" when to quit and give it back to the human?
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#83Earlier quoted context omitted.
> Your accountant is not responsible for knowing all the intimate details of your business. You need to tell them! Your accountant as a 3rd party might have this issue. Your accountant that you hire as an employee to help you run your business is the one who should be doing this.
An LLM agent is strongly third party.
So it's not going to replace engineers or CS? Because thats how they are being sold right now.
If it is a third party then your vibe coding or getting CS from a random on a reddit thread (effectively).
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#84Posts like this kinda-sorta grind my gears, like... I get it, but also... accounting, like many real world tasks, is fundamentally a chain of precise and constrained and auditable operations. Humans approach these tasks through structured processes... we use roles, and we have checkpoints precisely because complexity compounds quickly and becomes unmanageable if tackled as one giant block. Expecting a single AI model to handle an e2e workflow seamlessly without similarly explicit segmentation and oversight misunderstands not only the model but also the nature of the workflow itself.
I wanna see someone take long horizon tasks, recongnize they're not linear, and design and test a better system: structured orchestration, transparent auditability, and disciplined modularity, I think that would be considerably more interesting personally.
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#85Hey all, member of the benchmark team here! The goal for this project was to see how LLMs well could do bookkeeping without an overly opinionated scaffold. We gave them access to processed transaction records and code execution tools, but it was up to them to choose exactly how to use those. Claude and Grok 4 did reasonably well (within CPA baselines) for the first few months, but tended to degrade as more data came…
It's a start. The world needs a better way to handle bookkeeping, and the existing tools sure aren't cutting it. Bookkeeping for my small business runs into the tens of thousands of dollars every year, and the amount of human error associated with processing assorted ecommerce and other transactions is astounding, even after extensive planning and SOPs. The other pain point is Quickbooks. The tool is so sprawling and…
> It's a start. The world needs a better way to handle bookkeeping, and the existing tools sure aren't cutting it.
God, please, no. Non-deterministic language models aren't the solution to improve bookkeeping.
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#86But can't it, literally, hallucinate raw data at any point in the run?
Alls LLM have this risk but somehow nobody seems to care or they think they can order the LLM to stop with a better prompt.
If it was as simple as telling the LLM not to hallucinate every system prompt would just say "don't hallucinate" and we wouldn't have hallucinations
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#87Hey all, member of the benchmark team here! The goal for this project was to see how LLMs well could do bookkeeping without an overly opinionated scaffold. We gave them access to processed transaction records and code execution tools, but it was up to them to choose exactly how to use those. Claude and Grok 4 did reasonably well (within CPA baselines) for the first few months, but tended to degrade as more data came…
It's a start. The world needs a better way to handle bookkeeping, and the existing tools sure aren't cutting it. Bookkeeping for my small business runs into the tens of thousands of dollars every year, and the amount of human error associated with processing assorted ecommerce and other transactions is astounding, even after extensive planning and SOPs. The other pain point is Quickbooks. The tool is so sprawling and…
With no context of what your business is, I hated QuickBooks, love Xero though.
There's some other alternatives too, Zoho, freshbooks.
Really depends what you do.
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#88Posts like this kinda-sorta grind my gears, like... I get it, but also... accounting, like many real world tasks, is fundamentally a chain of precise and constrained and auditable operations. Humans approach these tasks through structured processes... we use roles, and we have checkpoints precisely because complexity compounds quickly and becomes unmanageable if tackled as one giant block. Expecting a single AI model…
It's a useless benchmark if everyone aces it. If some models do better than others and none saturate it, then is has some value, no? Permitting comparison is the point.
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#89> Agent: This is getting too complex with the sign errors. Let me just find a historical transaction that would make up the difference
Haha, this strongly reminds me of doing TDD with Claude
Re: AccountingBench: Evaluating LLMs on real long-horizon business tasks
#90This is cool. A bunch of interesting things here:
1. Agent can create its own tools and save them to memory
2. You create a SQL (and web app?) workbench per agent run
3. Grok fell off a cliff in the last month. Was this consistent over multiple runs?
4. Agents have a difficult time backtracking. Would unwinding system state and agent context make backtracking better? (Harder to implement this, though)
5. Since each new month only uses final state from previous month, agent has no way to understand why error occurred in previous month