When Rubrics Change: Cross-Rubric Generalization for Critical Thinking Essay Scoring

Cross-rubric generalization for automated essay scoring using LLM fine-tuning with trait-based representations

A new arXiv paper introduces a framework for automated essay scoring (AES) that generalizes to previously unseen scoring rubrics, rather than just unseen prompts. The approach uses rubric-agnostic intermediate representations called 'traits' combined with target-essay supervision, achieving a 5.0% macro F1 improvement over a baseline in the hardest generalization setting. A fine-tuned Llama-based model outperforms GPT-5-mini prompting by 2.1% macro F1 and trails GPT-5 by only 1.9%, demonstrating that structured intermediate representations improve rubric generalization.