3 ms·

> You're right about the difference between one-by-one prompts and prompts that create a population. I switched to sets of 10 at a time and it got better. But

> You're right about the difference between one-by-one prompts and prompts that create a population. I switched to sets of 10 at a time and it got better. But still, when you ask for "make up a family", the model should not interpret that as "pick the most likely family". I disagree with your opinion that it's hard. GPT does not work by creating a pool of possible families and then sampling them; it works by picking the next set of words based on the prompt and probabilities. If "Dr. Laura Nguyen and Robert Smith, an unemployed actor" is 1% likely, it should come up 1% of the time. The sampling is built in to the system.

tick_tock_tick 3y ago
> But still, when you ask for "make up a family", the model should not interpret that as "pick the most likely family". But that's literally what LLMs do.... You don't get a choice with this technology.
PeterisP 3y ago
No, the sampling does not work like that, that way lies madness (or poor results). The models oversample the most likely options and undersample rare options. Always picking the most likely option leads to bad outcomes, and literally sampling from the actual probability distribution of the next word also leads to bad outcomes, so you want something in the middle and for that tradeoff there's a configurable "temperature" parameter, or in some cases "top-p" parameter where sampling is done only from a few of the most likely options, and rare options have 0 chance to be selected. Of course that parameter doesn't only influence the coherency of text (for which it is optimized) but also the facts it outputs; so it should not (and does not) always "pick the most likely family", but it would be biased towards common families (picking them even more commonly than they are) and biased against rare families (picking them even more rarely than they are). But if you want it to generate a more varied population, that's not a problem, the temperature should be trivial to tweak.
systems_glitch 3y ago
I have a somewhat shallow understanding of LLMs due basically to indifference, but isn't "pick the most likely" literally what it's designed to do?

tick_tock_tick 3y ago

> But still, when you ask for "make up a family", the model should not interpret that as "pick the most likely family". But that's literally what LLMs do.... You don't get a choice with this technology.

PeterisP 3y ago

No, the sampling does not work like that, that way lies madness (or poor results). The models oversample the most likely options and undersample rare options. Always picking the most likely option leads to bad outcomes, and literally sampling from the actual probability distribution of the next word also leads to bad outcomes, so you want something in the middle and for that tradeoff there's a configurable "temperature" parameter, or in some cases "top-p" parameter where sampling is done only from a few of the most likely options, and rare options have 0 chance to be selected. Of course that parameter doesn't only influence the coherency of text (for which it is optimized) but also the facts it outputs; so it should not (and does not) always "pick the most likely family", but it would be biased towards common families (picking them even more commonly than they are) and biased against rare families (picking them even more rarely than they are). But if you want it to generate a more varied population, that's not a problem, the temperature should be trivial to tweak.

systems_glitch 3y ago

I have a somewhat shallow understanding of LLMs due basically to indifference, but isn't "pick the most likely" literally what it's designed to do?