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arXiv Computation and Language

Trait-Aware Policy Optimization for Autoregressive Multi-Trait Essay Scoring

The paper introduces Trait-Aware Policy Optimization (TAPO), a post‑training framework for autoregressive models that score essays across multiple traits. TAPO decomposes rewards by sample and trait, integrating global consistency, trait accuracy, format validity, and inter‑trait dependencies, while enriching prompts with trait descriptions. Experiments on various backbone models show TAPO consistently outperforms supervised fine‑tuning and scalar‑reward baselines, proving its effectiveness and transferability for multi‑trait essay scoring.

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arXiv Computation and Language
Aug 31

A Unified Framework to Elicit Structured Feedback for Interpretable Multi-Trait Essay Scoring

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++.

By Shihang Yang, Sanwoo Lee, Ningning Zhao, Yunfang Wu
llmssafety
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Hugging Face Trending Papers
Jul 21

Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards

Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while systematic research on reinforcement learning (RL) post-training and automated evaluation of feedback quality remains limited.

llmsreinforcement-learningfine-tuningbenchmarks
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arXiv Machine Learning
Sep 4

SWIM: Student Writing Simulation via Proficiency-Conditioned Generation

The paper introduces SWIM, a task that frames student writing simulation as proficiency‑conditioned essay generation. It evaluates prompting, supervised fine‑tuning, and reinforcement learning for aligning generated essays with student proficiency profiles, using automated essay scoring as a metric. Results show that prompting alone offers limited control, while supervised fine‑tuning and reinforcement learning significantly improve alignment across content, lexical, grammatical, and organizational traits, though low‑proficiency writing remains difficult to replicate.

By Heejin Do, Jakub Kontak, Mrinmaya Sachan
llmsreinforcement-learningfine-tuningsafety
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arXiv AI
Aug 26

Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring

The paper introduces a cost‑aware framework that treats each prompt type as an arm in a multi‑armed bandit controller, enabling adaptive selection of optimal prompting strategies during inference for automated essay scoring. Experiments on IELTS Writing Task 2 essays demonstrate that this bandit-driven approach achieves comparable scoring accuracy to exhaustive grid search while reducing LLM calls by 78.4%. The study also presents the first cost‑reliability learning curves for essay scoring, offering actionable insights for educational technology platforms balancing operational costs against assessment validity.

By Olga Manakina, Igor Bogdanov
llmsreinforcement-learning
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