# RDBE: Reasoning Distillation-Based Evaluation Enhances Automatic Essay Scoring

arXiv: https://arxiv.org/abs/2407.13781 · HTML: https://mlsanity.com/paper/2407.13781/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)
- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://mlsanity.com/paper/2305.02301/) (2023)
- [Large Language Models Are Reasoning Teachers](https://mlsanity.com/paper/2212.10071/) (2022)
- [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://mlsanity.com/paper/2112.07916/) (2021)
- [On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation](https://mlsanity.com/paper/2205.03835/) (2022)
- [Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays](https://mlsanity.com/paper/2102.00781/) (2021)
- [Autoregressive Score Generation for Multi-trait Essay Scoring](https://mlsanity.com/paper/2403.08332/) (2024)
- [DREsS: Dataset for Rubric-based Essay Scoring on EFL Writing](https://mlsanity.com/paper/2402.16733/) (2024)

## Abstract (arXiv)

Recently, various encoder-only and encoder-decoder pre-trained models like BERT and T5 have been applied to automatic essay scoring (AES) as small language models. However, existing studies have primarily treated this task akin to a classification problem, focusing solely on outputting scores in the target text without offering interpretations for the generated scores. Departing from the approaches, we introduce Reasoning Distillation-Based Evaluation (RDBE), which integrates interpretability to elucidate the rationale behind model scores while enhancing performance through initial reasoning. This interpretive capability is acquired during training by leveraging generated reasoning from a large language model (LLM) to distill a small language model (SLM). Our experimental results demonstrate the efficacy of RDBE across all scoring rubrics considered in the dataset. RDBE outperforms both zero-shot LLM generation and generation from a baseline fine-tuned model, establishing itself as state-of-the-art in the corresponding dataset. This highlights its practical interpretative output and enhanced performance.