Statistical Foundations for LLM Development and Evaluation

← Back to Blog & Articles When the same model gets a different score on a second run, the difference is not a mystery. […]

← Back to Blog & Articles A model that says it is 90% confident should be right about 90% of the time. Calibration measures […]

← Back to Blog & Articles If a model has seen test questions during training, its score reflects memory instead of ability. Contamination is […]

← Back to Blog & Articles Using one language model to grade another is cheap and scalable. It is also a measurement instrument that […]

← Back to Blog & Articles Pairwise comparisons, such as human votes between two responses, can be turned into a ranking. Bradley–Terry and Elo […]

← Back to Blog & Articles If you run enough comparisons, some will look significant by chance. Leaderboards and ablation studies make this easy […]

← Back to Blog & Articles When two models answer the same questions, their results are linked. A paired analysis uses that link to […]

← Back to Blog & Articles A common failure in evaluation is a test set too small to detect the difference that matters. Power […]

← Back to Blog & Articles A score with no uncertainty attached invites over-reading. Confidence intervals are the simplest way to show how much […]

← Back to Blog & Articles A benchmark score looks like a fact about a model. Statistically, it is an estimate of an underlying […]