Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios
This paper introduces Elmes*, an end-to-end framework for automatically constructing, refining, and applying fine-grained evaluation rubrics for LLMs in educational contexts. It combines a multi-agent engine with a self-evolving module SceneGen to co-optimize evaluation criteria and test data. The resulting Edu-330 benchmark covers 330 scenarios across 11 subjects, 3 grade levels, and 10 task types with over 1,000 indicators. Experiments reveal that educational capability is multidimensional: top LLMs differ mainly in creativity and values integration, knowledge-strong models may fail at Socratic scaffolding, and the education-specialized InnoSpark achieves the best human-evaluated score. LLM judges preserve human-comparable rankings but exhibit biases like self-preference. The framework provides scalable diagnostic infrastructure for pedagogy-grounded LLM evaluation.
[2606.06546] Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios
[Submitted on 4 Jun 2026]
Title:Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios
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Abstract:Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know. Existing benchmarks emphasize domain-general correctness or depend on manually designed rubrics that scale poorly to long-tail pedagogical scenarios. We introduce Elmes*, an end-to-end framework for constructing, refining, and applying fine-grained scenario-specific rubrics. Elmes* combines a declarative multi-agent engine for teacher--student--judge interactions with SceneGen, a self-evolving module that co-optimizes evaluation criteria and test data from expert-defined pedagogical dimensions. Using Elmes*, we build Edu-330, covering 330 scenarios across 11 subjects, 3 grade bands, and 10 task types, with over 1{,}000 second-level indicators. Experiments on Edu-330 and four expert-authored gold-standard scenarios show that educational capability is multidimensional: top-tier LLMs differ mainly in creativity and values integration, knowledge-strong models may fail at Socratic scaffolding, and the education-specialized InnoSpark achieves the best human-evaluated average score. LLM judges preserve human-comparable rankings with much lower scoring variance, but exhibit judge-specific biases such as self-preference. Ablations show that expert-scored few-shot anchoring improves human--LLM alignment, while reasoning enforcement and greedy decoding are model-dependent. Elmes* thus provides scalable diagnostic infrastructure for pedagogically grounded LLM evaluation.
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Machine Learning (cs.LG)
Cite as: arXiv:2606.06546 [cs.LG]
(or arXiv:2606.06546v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06546
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From: Hao Hao [view email] [v1] Thu, 4 Jun 2026 07:40:12 UTC (1,672 KB)
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