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arXiv Computation and Language By Zhihao Wu, Linhai Zhang, Taiyi Wang, Runcong Zhao, Peter Andrews, Cesare Aloisi, Yulan He

EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading

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The paper introduces Evidence‑Diagnosed Intervention Training (EDIT), a two‑phase framework designed to improve rubric‑faithful grading by large language models. EDIT‑SFT first identifies problematic reasoning steps using internal model signals—posterior belief over the final mark and input‑grounding scores—and revises only those steps with rubric checklists. EDIT‑RL then calibrates the grader with belief‑guided reward shaping, penalising harmful belief drifts while encouraging useful exploration. Experiments on two real‑world, multi‑subject grading benchmarks show that EDIT consistently outperforms strong supervised fine‑tuning and reinforcement learning baselines, with ablation studies confirming the importance of internal‑state diagnostics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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