JEV-as-a-Judge: Accept When Confident, Escalate When Unsure

arXiv:2609.26550 · cs.AI · Submitted 2026-09-22 · Read on arXiv

cs.AI

Submitted: 2026-09-22

Updated: 2026-09-27

Terminology

• Accuracy — 72× in this paper · explained in 1 episode
• Benchmark — 31× in this paper · explained in 2 episodes
• LLM judge — 9× in this paper · explained in 2 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Hallucination — 8× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 7× in this paper · explained in 3 episodes
• Provenance — 6× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Deferral — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Abstention — 1× in this paper · explained in 1 episode
• Arbiter — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Judging LLM-as-a-Judge: Concerning Rubric Artifacts in LLM-based Automated Text Generation Evaluation
• Rethinking Verbalized Confidence for LLM-as-a-Judge: A Compatibility Shift on Post-2025 Proprietary Models

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