The main point didn't get hit on by the responses. Re-ranking is just a mini-LLM (for latency/cost reasons) that does a double heck. Embedding model finds the closest M documents in R^N space. Re-ranker picks the top K…
It's dead simple. Here is the simplified version that returns the quantiles for '100 / 2 ~ 4'. import numpy as np def monte_carlo(formula, iterations=100000): res = [formula() for _ in range(iterations)] return…
The main point didn't get hit on by the responses. Re-ranking is just a mini-LLM (for latency/cost reasons) that does a double heck. Embedding model finds the closest M documents in R^N space. Re-ranker picks the top K…
It's dead simple. Here is the simplified version that returns the quantiles for '100 / 2 ~ 4'. import numpy as np def monte_carlo(formula, iterations=100000): res = [formula() for _ in range(iterations)] return…