AINeutralarXiv – CS AI · Mar 34/106
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Confusion-Aware Rubric Optimization for LLM-based Automated Grading
Researchers introduce CARO (Confusion-Aware Rubric Optimization), a new framework that improves LLM-based automated grading by using confusion matrices to separate and fix specific error patterns instead of aggregating all errors together. This approach prevents conflicting constraints and significantly outperforms existing methods in teacher education and STEM datasets.