Share the Judge, Learn the Deferral: Where Specialization Helps LLM Evaluation
Agentic systems generate outputs faster than human review. We contrast two LLM evaluator specialization strategies: specialized judge weights, or rule-based deferral policies for safe judgment acceptance. On 99,952 rubric-conditioned samples, correct rubrics improve accuracy by 2.11 points, while incorrect rubrics reduce performance by 2.66 points. Splitting training data across eight criterion-specific LoRA experts lowers accuracy by 10.05 points and reduces 5% error-bound coverage from 24.44% to 5.43%. This loss is independent of model size and training settings, with most performance recoverable by warm-starting experts from a unified judge. Sweeping data budgets confirms scratch exper...
| Authors | Ye Chen, Weining Zhang |
|---|---|
| Primary category | cs.AI |
| Published | 30 July 2026 |
| Last updated at source | 21 August 2026 |
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