3arXiv cs.CL (Computation and Language)·
GPT-5 variants benchmarked for summarization-assisted automated essay scoring on ASAP 2.0
Researchers propose a hybrid framework for automated essay scoring (AES) that uses GPT-5, GPT-5 mini, and GPT-5 nano to generate controlled-length summaries of long essays, addressing transformer input-length limitations. Summaries are combined with handcrafted linguistic features and fed into downstream AES models evaluated on the ASAP 2.0 dataset using quadratic weighted kappa. GPT-5 mini achieves the best human-rating agreement while GPT-5 produces higher summarization quality, revealing cost-performance trade-offs. The study also finds that higher-scoring, more complex essays are harder to compress without information loss, raising fairness concerns for educational deployment.
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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.
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