A Unified Framework to Elicit Structured Feedback for Interpretable Multi-Trait Essay Scoring
Read the original on arXiv Computation and Language →The paper introduces HiFTS, a unified autoregressive framework that generates hierarchical chain-of-thought (CoT) feedback before predicting trait-level and holistic scores for multi-trait automated essay scoring. HiFTS distills rubric-grounded CoT feedback from a teacher large language model and trains student models to jointly produce feedback and scores, employing Group Relative Policy Optimization to balance score agreement, calibration, feedback quality, and structural validity. The authors also present CFMS-34, a new Chinese multi-trait AES dataset, and demonstrate that HiFTS achieves strong scoring performance while producing coherent, rubric-aligned feedback on CFMS-34 and ASAP++.
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.