The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations

arXiv:2608.18300 · cs.AI · Submitted 2026-08-18 · Read on arXiv

cs.AI

Submitted: 2026-08-18

Updated: 2026-08-31

Terminology

• Benchmark — 29× in this paper · explained in 2 episodes
• LLM judge — 22× in this paper · explained in 2 episodes
• Alignment — 19× in this paper · explained in 1 episode
• LLM-as-a-Judge — 10× in this paper · explained in 3 episodes
• Reasoning-Aligned Rubric Tuning — 4× in this paper
• Human-in-the-Loop — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Fourteenth International Conference on Learning Representations — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment

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