From Cases to Criteria: Reusing Evaluation Knowledge for Task-Specific Rubric Generation

Query-specific rubrics make LLM evaluation more fine-grained and transparent, and generating high-quality rubrics requires both identifying the target query’s evaluation scope and specifying concrete checks. Historical rubrics provide reusable evaluation experiences, yet existing approaches reuse either complete query–rubric cases or aggregated criteria and dimensions without explicitly connecting recurring evaluation requirements to their criterion-level evidence and source-query context. This can preserve irrelevant task details or lose the broader evaluation structure. In this paper, we introduce RubricDuo, which organizes historical query–rubric pairs into a Rubric Knowledge Graph linking evaluation dimensions, shared concepts, local concepts, criteria, and queries, where reusable evaluation requirements across queries are abstracted by shared concepts. Given a target query, RubricDuo first combines concepts relevant to the target query with retrieved historical cases to draft a rubric with broad coverage. It then revisits each draft criterion, retrieves concept-linked historical criteria and their source queries, and adapts applicable evidence to refine the criterion. Experiments on RubricBench and HealthBench demonstrate that RubricDuo achieves the overall best performance among the evaluated automatic methods in both alignment with human-authored rubrics and downstream LLM judging.