Pairwise evaluation presents two outputs for the same input and asks a reviewer or model which is better under a defined rubric. It is often easier than assigning absolute scores because the comparison is concrete. Results still depend on candidate order, reviewer consistency, ties, and whether the rubric matches the decision.
How does a pairwise evaluation run?
Generate two candidates from the same eval dataset, hide irrelevant identity, and judge them against explicit criteria. The unit of evidence is a preference for one matched pair. Aggregate many pairs with ties and uncertainty rather than turning a small sample into a precise ranking.
How do teams control bias?
Swap candidate order, randomize presentation, control obvious length differences, and calibrate automated judgments against people. An LLM as a judge needs its own evaluation before it can rank another system. Track judge bias by perturbing format and identity while holding content stable. Use model calibration when preference scores are interpreted as probabilities rather than simple wins.
Frequently asked questions
When is pairwise evaluation useful?
Use it to compare model or prompt versions when quality is subjective but reviewers can identify the better of two concrete outputs.
Should a pairwise evaluator allow ties?
Yes, when outputs are genuinely equivalent or the rubric does not distinguish them. Forced choices create false preference signal.
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