Agent Judges Match Humans, Reveal Scaling Laws
💡Scale LLM evals efficiently: log scores saturate fast, power-law for discoveries
⚡ 30-Second TL;DR
What Changed
Persona-based agent judges indistinguishable from humans in 960 sessions.
Why It Matters
Enables scalable, cost-effective LLM evaluations replacing large human panels. Guides optimal panel sizing: small for scores, larger for edge cases. Advances reliable agent-based judging in AI benchmarking.
What To Do Next
Build Big Five persona-conditioned agent judges for your LLM eval pipeline.
Key Points
- •Persona-based agent judges indistinguishable from humans in 960 sessions.
- •Scores scale logarithmically, discoveries via sublinear power law.
- •Diminishing returns: scores saturate twice as fast as discoveries.
- •Big Five traits drive ensemble diversity; ablation confirms need for structured conditioning.
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Original source: ArXiv AI ↗
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