LLM Judge Validation Under Sparse Overlap: From Inference to Design

arXiv:2609.31857 · cs.AI, stat.AP · Submitted 2026-09-25 · Read on arXiv

cs.AI, stat.AP

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Benchmark — 32× in this paper · explained in 2 episodes
• LLM judge — 17× in this paper · explained in 2 episodes
• Sparsity — 12× in this paper · explained in 4 episodes
• LLM-as-a-Judge — 8× in this paper · explained in 3 episodes
• Core — 6× in this paper · explained in 2 episodes
• Annotator Agreement — 5× in this paper · explained in 1 episode
• Calibration — 4× in this paper · explained in 7 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Clustering — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Conformal Prediction — 2× in this paper · explained in 5 episodes
• Institutional Review Board — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• AI Alignment — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Exchangeability — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Large Language Models — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Counting on Consensus: Selecting the Right Inter-annotator Agreement Metric for NLP Annotation and Evaluation
• LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods

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