# Improve LLM-based Automatic Essay Scoring with Linguistic Features

arXiv: https://arxiv.org/abs/2502.09497 · HTML: https://mlsanity.com/paper/2502.09497/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [GPTScore: Evaluate as You Desire](https://mlsanity.com/paper/2302.04166/) (2023)
- [LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts](https://mlsanity.com/paper/2501.00274/) (2024)
- [Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation](https://mlsanity.com/paper/2404.15845/) (2024)
- [On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation](https://mlsanity.com/paper/2205.03835/) (2022)
- [Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay Scoring](https://mlsanity.com/paper/2008.01441/) (2020)
- [Personalized Text Generation with Fine-Grained Linguistic Control](https://mlsanity.com/paper/2402.04914/) (2024)
- [A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation](https://mlsanity.com/paper/2406.15227/) (2024)

## Built on by (most cited first, 8 of 8)

- [SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models](https://mlsanity.com/paper/2505.07247/) (2025)
- [How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding](https://mlsanity.com/paper/2603.18009/) (2026)
- [SEFORA: Student Essays with Feedback Corpus and LLM Feedback Evaluation Framework](https://mlsanity.com/paper/2607.00274/) (2026)
- [Decision-Level Ordinal Modeling for Multimodal Essay Scoring with Large Language Models](https://mlsanity.com/paper/2603.14891/) (2026)
- [From Feature-Based Models to Generative AI: Validity Evidence for Constructed Response Scoring](https://mlsanity.com/paper/2603.19280/) (2026)
- [Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education](https://mlsanity.com/paper/2605.21614/) (2026)
- [AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not](https://mlsanity.com/paper/2608.27855/) (2026)
- [GRASP: Graph-Retrieval Automated Scoring Pipeline for Label-Free Multi-Topic Essay Grading](https://mlsanity.com/paper/2609.03857/) (2026)

## Abstract (arXiv)

Automatic Essay Scoring (AES) assigns scores to student essays, reducing the grading workload for instructors. Developing a scoring system capable of handling essays across diverse prompts is challenging due to the flexibility and diverse nature of the writing task. Existing methods typically fall into two categories: supervised feature-based approaches and large language model (LLM)-based methods. Supervised feature-based approaches often achieve higher performance but require resource-intensive training. In contrast, LLM-based methods are computationally efficient during inference but tend to suffer from lower performance. This paper combines these approaches by incorporating linguistic features into LLM-based scoring. Experimental results show that this hybrid method outperforms baseline models for both in-domain and out-of-domain writing prompts.