The Geometry of LLM-as-Judge: Why Inter-LLM Consensus Is Not Human Alignment

arXiv:2606.03043 · cs.CL · Submitted 2026-06-02 · Read on arXiv

cs.CL

Submitted: 2026-06-02

Updated: 2026-09-02

Terminology

• LLM judge — 25× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Alignment — 14× in this paper · explained in 1 episode
• Calibration — 13× in this paper · explained in 7 episodes
• Hallucination — 9× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Intrinsic Dimension — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes

Sources

• Systematic Inequalities in Language Technology Performance across the World's Languages
• Gemini: A Family of Highly Capable Multimodal Models
• The Llama 3 Herd of Models
• The State and Fate of Linguistic Diversity and Inclusion in the NLP World
• Scaling Laws for Neural Language Models
• Qwen3 Technical Report

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