How do you measure whether a model is actually good? The answer is a set of metrics — precision, recall, F1, perplexity, calibration, confidence intervals — each measuring something different and failing in a different way.
Language models are probabilistic systems. Understanding the statistical machinery behind maximum likelihood estimation, Bayesian inference, and sampling algorithms clarifies why training and decoding work the way they do.
How do you measure whether a model is actually good? The answer is a set of metrics — precision, recall, F1, perplexity, calibration, confidence intervals — each measuring something different and failing in a different way.
Language models are probabilistic systems. Understanding the statistical machinery behind maximum likelihood estimation, Bayesian inference, and sampling algorithms clarifies why training and decoding work the way they do.