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But, if you ran, gave the problem to all the top models and got the same? Is it still likely an incorrect answer? I checked 6. I checked a bunch of times. Different accounts. I was testing it. I'm seeing if its possible with all that in others opinions I actually had to check over a hundred times each got the same numbers.
They could get the right answer 9999 times out of 10000 and that one wrong answer is enough to make all the correct answers suspect.
What if there is a popular joke that relies on bad math that happens to be your question. Then the alignment is understandable and no indication of accuracy. Why use a tool with known issues, and overhead like querying six, instead of using a decent tool like Wolfram alpha?
I did dozens of times. Same calculations.
That doesn't change the logic I gave
my use case was, i expect easier and simpler. so i was able to write automated tests to validate logic of incrementing specific parts of a binary number and found that expected test values llm produced were wrong.
so if its possible to use some kind of automation to verify llm results for your problem, you will be confident in your answer. but generally llms tend to make up shit and sound confident about it