Definition
In RAG and AI agent architectures, Expectation-Maximization (EM) is an iterative optimization framework used when the identity of the 'best' retrieved document is an unobserved latent variable. It enables joint training of the retriever and reader by alternating between estimating the probability that a retrieved document supports the correct output (E-step) and updating model parameters to maximize that probability (M-step).
Refers to latent variable optimization in model training, not the 'Exact Match' evaluation metric.
"A scout bringing back several maps to a navigator; the navigator identifies which map actually matches the terrain (E-step), and the scout then learns to look for maps with those specific features in the future (M-step)."
- Latent Variable(Component)
- Marginal Likelihood(Prerequisite)
- Joint Training(Prerequisite)
- Dense Passage Retrieval (DPR)(Component)
Conceptual Overview
In RAG and AI agent architectures, Expectation-Maximization (EM) is an iterative optimization framework used when the identity of the 'best' retrieved document is an unobserved latent variable. It enables joint training of the retriever and reader by alternating between estimating the probability that a retrieved document supports the correct output (E-step) and updating model parameters to maximize that probability (M-step).
Disambiguation
Refers to latent variable optimization in model training, not the 'Exact Match' evaluation metric.
Visual Analog
A scout bringing back several maps to a navigator; the navigator identifies which map actually matches the terrain (E-step), and the scout then learns to look for maps with those specific features in the future (M-step).