LLM Evaluation, Custom Benchmarks & Model Arenas
Design domain benchmarks, task sets, and judging protocols. Use blind testing and pairwise comparisons to assess capability, cost, and latency.
Updated October 10, 2026
Define capability through real tasks
General leaderboards provide context, while domain selection requires representative tasks. Document sample sources, task categories, and scoring methods, and keep evaluation data separate from training data.
Blind tests, pairwise comparison, and judging protocols
For open-ended responses, blind testing and pairwise comparison can reduce bias from model names. Specify quality criteria, tie handling, and human review in the judging protocol.
Measure quality, cost, and latency together
Model selection requires more than an aggregate score. Record completion rates, failure types, inference settings, operating cost, and latency for comparable results and regression evaluation after updates.
Common questions
How do custom benchmarks differ from general leaderboards?
Custom benchmarks target a specific domain, task, and acceptance criteria. General leaderboards cover broader capabilities but may not predict performance in a particular application.
Can an arena ranking determine model selection on its own?
No. Interpret pairwise preferences alongside task coverage, sample size, judging methods, cost, and latency.