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VCs are starting to partner with private equity to buy up call centers, accounting firms and other "mature companies" to replace their operations with AI (techcrunch.com)
submitted 1 year ago by to c/technology@lemmy.world
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[–] 5 points 1 year ago (2 children)

For usage like that you'd wire an LLM into a tool use workflow with whatever accounting software you have. The LLM would make queries to the rigid, non-hallucinating accounting system.

I still don't think it would be anywhere close to a good idea because you'd need a lot of safeguards and also fuck your accounting and you'll have some unpleasant meetings with the local equivalent of the IRS.

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  • [–] 4 points 1 year ago (1 child)

    The LLM would make queries to the rigid, non-hallucinating accounting system.

    And then sometimes adds a halucination before returning an answer - particularly when it encournters anything it wasn't trained on, like important moments when business leaders should be taking a closer look.

    There's not enough popcorn in the world for the shitshow that is coming.

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  • [–] 0 points 1 year ago (1 child)

    You're misunderstanding tool use, the LLM only queries something to be done then the actual system returns the result. You can also summarize the result or something but hallucinations in that workload are remarkably low (however without tuning they can drop important information from the response)

    The place where it can hallucinate is generating steps for your natural language query, or the entry stage. That's why you need to safeguard like your ass depends on it. (Which it does, if your boss is stupid enough)

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  • [–] 1 point 1 year ago* (last edited 1 year ago)

    I'm quite aware that it's less likely to technically hallucinate in these cases. But focusing on that technicality doesn't serve users well.

    These (interesting and useful) use cases do not address the core issue that the query was written by the LLM, without expert oversight, which still leads to situations that are effectively halucinations.

    Technically, it is returning a "correct" direct answer to a question that no rational actor would ever have asked.

    But when a halucinated (correct looking but deeply flawed) query is sent to the system of record, it's most honest to still call the results a halucination, as well. Even though they are technically real data, just astonishingly poorly chosen real data.

    The meaningless, correct-looking and wrong result for the end user is still just going to be called a halucination, by common folks.

    For common usage, it's important not to promise end users that these scenarios are free of halucination.

    You and I understand that technically, they're not getting back a halucination, just an answer to a bad question.

    But for the end user to understand how to use the tool safely, they still need to know that a meaningless correct looking and wrong answer is still possible (and today, still also likely).

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