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AI Research
arXiv CS.AI·14d ago·5 min read
Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards
TL;DR
RLAES is a novel framework that enhances automated essay scoring and feedback generation using reinforcement learning, achieving superior scoring performance and maintaining high feedback quality. It introduces a rubric-based evaluation system that effectively measures and optimizes feedback.
✦ Why It Matters
Implement RLAES to enhance your essay scoring systems with better feedback mechanisms today.
Key Takeaways
- Explore RLAES for improved essay scoring and feedback generation.
- Utilize Rubric-based Feedback Evaluation for measurable feedback quality.
- Implement Adaptive Gated Feedback Optimization to reduce evaluation overhead.
- Adopt Adjacent Contrastive Reasoning for better score calibration.
How It Works
RLAES employs reinforcement learning to optimize essay scoring and feedback generation simultaneously. It uses Rubric-based Feedback Evaluation (RFE) to provide a structured way to assess feedback quality, allowing for fine-tuned adjustments based on 166 specific rubric items.
Adaptive Gated Feedback Optimization (AGFO) selectively activates feedback rewards during the learning process, which helps in maintaining high-quality feedback while reducing the computational burden. Adjacent Contrastive Reasoning (ACR) enhances the model's ability to differentiate between closely ranked scores, improving overall scoring accuracy.