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This paper introduces Elmes+, an automated framework for constructing fine-grained evaluation rubrics for LLMs in long-tail educational scenarios, and presents the Edu-330 benchmark covering 330 scenarios across 11 subjects. The framework uses a multi-agent engine and self-evolving module to co-optimize evaluation criteria and test data, revealing multidimensional educational capability differences among top LLMs.
This paper proposes learning assessment skills for LLMs to automate rubric construction for scoring tasks, achieving performance comparable to expert-written rubrics without requiring human-written examples.