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Twirrim • yesterday at 8:05 PM • 3 replies • view on HN

The frustrating part about an LLM is at a fundamental level it's designed to do the wrong thing, it's designed to provide an answer for things it can't do, e.g. one argument I've been in recently at work is someone using it to summarise numerical data. They've a raw data spreadsheet they're putting into ChatGPT and asking it to produce a report in a particular style. So the LLM is doing its job, predicting the tokens to emit, and not actually doing mathematical analysis of the underlying data.

I had a team proudly show me how they were feeding their dashboard data into an LLM to predict growth needs, instead of doing basic linear regression or any number of pre-existing basic statistical analysis. The part that depressed me the most on that was it was a bunch of engineers that did it, the people you'd most hope would understand what an LLM is/does.


Replies

anitil • today at 3:34 AM

These days I wouldn't be surprised if the models are smart enough to get the analysis correct.

However sometimes I think that AI use shows a revealed preference around things. In this case, the actual data just ... maybe never mattered to them at all? People wanted a shiny chart and they got a shiny chart.

DougBTX • yesterday at 8:22 PM

For numerical work, there’s no reason to assume the computation is happening in the LLM. All frontier models could handily write a Python script to do linear regression then report the result, so the quality of the responses here depends entirely on the prompt + harness.

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Foobar8568 • yesterday at 9:17 PM

Either chatgpt or claude are building scripts to do those and has been for a few months at least now.