13 ms·
This is a great write up! I nodded my head thru the whole post. Very much aligns with our experience over the past year. I wrote a simple example (overkiLLM) o
by mritchie712 3y ago
This is a great write up! I nodded my head thru the whole post. Very much aligns with our experience over the past year.
I wrote a simple example (overkiLLM) on getting reliable output from many unreliable outputs here[0]. This doesn't employ agents, just an approach I was interested in trying.
I choose writing an H1 as the task, but a similar approach would work for writing any short blob of text. The script generates a ton of variations then uses head-to-head voting to pick the best ones.
This all runs locally / free using ollama.
0 - https://www.definite.app/blog/overkillm https://www.definite.app/blog/overkillm
- maciejgryka 3y agoOh this is fun! So you basically define personalities by picking well-known people that are probably represented in the training data and ask them (their LLM-imagined doppelganger) to vote?
- CuriouslyC 3y agoIn the research literature, this process is done not by "agent" voting but by taking a similarity score between answers, and choosing the answer that is most representative. Another approach is to use multiple agents to generate a distribution over predictions, in sort of like bayesian estimation.
- infecto 3y agoFor clarification on the first part. The research suggests you can utilize the same prompt over multiple runs as the input to picking the answer.
- mistermann 3y agoAny chance you could expand on both of these, even enough to assist in digging deeper into them? TIA.
- CuriouslyC 3y agoThe TLDR is you can prompt the LLM to take different perspectives than its default, then combine those. If the LLM is estimating a number, the different perspectives give you a distribution over the truth, which shows you the range of biases and the most likely true answer (given wisdom of the crowd). If the LLM is generating non-quantifiable output, you can find the "average" of the answers (using embeddings or other methods) and select that one.
- mistermann 3y agoAh ok, so both are implemented via a call(s) to the LLM, as opposed to a standard algorithmic approach?
- CuriouslyC 3y agoOnce you have bayesian prior distributions (which it makes total sense for llms to estimate) you can do tons of nifty statistical techniques. It's only the bottom layer of the analysis stack that's LLM generated.
- mritchie712 3y agofor my use case (generating an interesting H1), using a similarity score would defeat the purpose. In this approach, I'm looking for the diamond in the rough. It's often dissimilar from the others. With this approach, the diamond can still get a high number of votes.
- CuriouslyC 3y agoThat approach definitely has promise. I would have agents rate answers and take the highest rated rather than vote for them though, since you're losing information about ranking and preference gradients with n choose 1. Also, you can do that whole process in one prompt, in case you're re-prompting currently, it's cheaper to batch it up.
- all2 3y agoI'd be curious to see some examples and maybe intermediate results?
- mritchie712 3y agohere's some examples[0]: this one scored high: Pinned Down - Powerful Analytics Without the Need for Engineering or SQL this one scored low: Analytics Made Accessible for Everyone. Each time I've compared the top scoring results to those at the bottom, I've always preferred the top scoring variations. 0 - https://docs.google.com/spreadsheets/d/1hdu2BlhLcLZ9sruVW8a_3IXVPonPV6PpbrupTF77MY8/edit#gid=2100975210 https://docs.google.com/spreadsheets/d/1hdu2BlhLcLZ9sruVW8a_...
- all2 3y agoI love the spreadsheet. That's exactly what I was looking for. Thank you!