Ask ChatGPT to estimate the carbs in your lunch. Now ask it again. And again. Five hundred times. You’d expect the same answer each time. It’s the same photo, the same model, the same question. But you won’t get the same answer. Not even close — and the differences are large enough to cause a
Mine would be: “I have no idea” - An answer the LLMs generally refuse to give by their nature (usually declining to answer is rooted in something in the context indicating refusing to answer being the proper text).
If you really pressed them, they’d probably google each thing and sum the results, so the estimates would be as consistent as first google results.
LLMs have a tendency to emit a plausible answer without regard for facts one way or the other. We try to steer things by stuffing the context with facts roughly based on traditional ‘fact’ based measures, but if the context doesn’t have factual data to steer the output, the output is purely based on narrative consistency rather than data consistency. It may even do that if the context has fact based content in it sometimes.
Mine would be: “I have no idea” - An answer the LLMs generally refuse to give by their nature (usually declining to answer is rooted in something in the context indicating refusing to answer being the proper text).
If you really pressed them, they’d probably google each thing and sum the results, so the estimates would be as consistent as first google results.
LLMs have a tendency to emit a plausible answer without regard for facts one way or the other. We try to steer things by stuffing the context with facts roughly based on traditional ‘fact’ based measures, but if the context doesn’t have factual data to steer the output, the output is purely based on narrative consistency rather than data consistency. It may even do that if the context has fact based content in it sometimes.