You don't need to hit a LLM multiple times to get multiple distributions, just provide a list of perspectives and ask the model to answer the question from each of them in turn, then combine the results right there in the prompt. I have tested this approach a bunch, it works.
> You don't need to hit a LLM multiple times to get multiple distributions
This isn't correct.
You're just sampling a different distribution.
You can adjust the shape of the distribution with your prompt; certainly... and if you make a good prompt, perhaps, you can narrow the 'solution space' that you sample into.
...but, you're still sampling randomly into a distribution, and the N'th token relies on the (N-1)'th token as an input; that means that a random deviance to a bad solution is incrementally responsible for a bad solution, regardless of your prompt.
...
Consider the prompt "Your name is Pete. What is your name?"
Seems like a fairly narrow distribution right?
However, there's a small chance that the first generated token is 'D'; it's small, but non-zero. That means it happens from time to time. The higher the temperature, the higher the randomization of the output tokens.
How do you imagine that completion runs when it happens? Doug? Dane? Danial? Dave? Don't know? I tell you what it is not; it's not Pete.
That's the issue here; when you sample, the solution space is wide, and any single sample has a P chance of being a stupid hallucination.
When you sample multiple times, the chance of that hallucination is P * P * P * P, etc. by the number of time you sample.
You can therefore control your error rate this way, because, you can calculate the chance of failure as P^N.
Yes, obviously, if your P(good answer) < P(bad answer) it has the opposite effect.
...but no, sampling once with a single prompt does not save you from this prompt no matter what or how good your prompt is.
Furthermore, when you evaluating prompts, only sampling once means you have no way of knowing if it was a good prompt or not. While, if you sample say, 10 times, you can see that obviously, from the outputs (eg. Pete, Pete, Pete, Pete, Potato, Pete, Pete <--- ) what the prompt is doing.
You can measure the error rate in your prompts this way.
If you don't, honestly, you really have no idea if your prompts are any good at all. You're just guessing.
People who run a prompt, tweak it, run it, tweak it, run it, tweak it, etc. are observing random noise, not doing prompt engineering.
I suggest you spend 20 hours evaluating the results of 10 prompts vs 1 prompt with multiple perspectives to learn the truth about the matter rather than trying to armchair expert.
Edit in response to your wall of text: I have *extensively* tested the results of multi-shot prompting vs repeated single shot prompting, and the differences between them are not material to the outcome of "averaging" results, or selecting the best result. You can theorize all you want, but the real world would like a word.
That's an early step that matters more when hitting chat with hidden temperature. Once you get a prompt dialed in you usually want to lower the temperature to the minimum value that still produces the desired results.
I will say though, using temperature 0 without understanding it (or worse, testing at temp > 0 and then setting temp to 0 for production, which I literally had to stop someone I know and respect as a developer from doing) and not understanding what top_k and top_n do (but using them anyway) is my #3 for LLM fails.
/shrug
...but, yes as you say, in a trivial case, like binary decision making, a 0 or very low temperature can help with the need to multiple sample; and as you say, when it's deterministic, sampling multiple times doesn't help at all.
What are some good metrics to evaluate LLM output performance in general? Or is it too hard to quantify at this stage (or not understood well enough). Perhaps the latter, or else those could be in the loss function itself..
