What This Investigation Covers
Real-world LLM deployments are evaluated on several dimensions simultaneously — quality, safety, latency, cost, refusal rate, and more. When models don’t agree across metrics, picking a winner requires making values explicit. This investigation covers the statistical and conceptual tools for multi-metric comparison: how to carry uncertainty through composite scores, when Pareto dominance lets you sidestep weighting choices, and how to present tradeoffs honestly to stakeholders.
What you’ll learn
Why composite scores can mislead: How the choice of weights drives the composite winner, and why hiding that choice inside an aggregate obscures a values decision that should be made explicitly.
Pareto dominance as a weight-free criterion: What it means for one model to dominate another across all metrics simultaneously, how to identify the Pareto frontier, and when it is (and isn’t) small enough to be useful.
Carrying uncertainty through multiple metrics: How to compute and visualize per-metric confidence intervals, detect when metric CIs are too wide to support any conclusion, and report results without false precision.
Presenting multi-metric results to stakeholders: Chart patterns and table formats that surface tradeoffs rather than a pre-digested verdict, and how to make weighting assumptions visible rather than baked in.
Looking for quick usage examples? Check out the Example Usage page.
Full worked examples, interactive code, and simulation-backed results are coming soon. Follow on GitHub for updates.