From Prompting to Preference Optimization: A Comparative Study of LLM-based Automated Essay Scoring
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arXiv
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| Format: | Preprint |
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2026
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| author | Nguyen, Minh Hoang Pham, Vu Hoang Huynh, Xuan Thanh Mai, Phuc Hong Nguyen, Vinh The Huynh, Quang Nhut Nguyen, Huy Tien Le, Tung |
| author_facet | Nguyen, Minh Hoang Pham, Vu Hoang Huynh, Xuan Thanh Mai, Phuc Hong Nguyen, Vinh The Huynh, Quang Nhut Nguyen, Huy Tien Le, Tung |
| contents | Large language models (LLMs) have recently reshaped Automated Essay Scoring (AES), yet prior studies typically examine individual techniques in isolation, limiting understanding of their relative merits for English as a Second Language (L2) writing. To bridge this gap, we presents a comprehensive comparison of major LLM-based AES paradigms on IELTS Writing Task~2. On this unified benchmark, we evaluate four approaches: (i) encoder-based classification fine-tuning, (ii) zero- and few-shot prompting, (iii) instruction tuning and Retrieval-Augmented Generation (RAG), and (iv) Supervised Fine-Tuning combined with Direct Preference Optimization (DPO) and RAG. Our results reveal clear accuracy-cost-robustness trade-offs across methods, the best configuration, integrating k-SFT and RAG, achieves the strongest overall results with F1-Score 93%. This study offers the first unified empirical comparison of modern LLM-based AES strategies for English L2, promising potential in auto-grading writing tasks. Code is public at https://github.com/MinhNguyenDS/LLM_AES-EnL2 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06424 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Prompting to Preference Optimization: A Comparative Study of LLM-based Automated Essay Scoring Nguyen, Minh Hoang Pham, Vu Hoang Huynh, Xuan Thanh Mai, Phuc Hong Nguyen, Vinh The Huynh, Quang Nhut Nguyen, Huy Tien Le, Tung Computation and Language 68T50, 68U10, 68T05 I.2.7; I.2.6; K.3.1 Large language models (LLMs) have recently reshaped Automated Essay Scoring (AES), yet prior studies typically examine individual techniques in isolation, limiting understanding of their relative merits for English as a Second Language (L2) writing. To bridge this gap, we presents a comprehensive comparison of major LLM-based AES paradigms on IELTS Writing Task~2. On this unified benchmark, we evaluate four approaches: (i) encoder-based classification fine-tuning, (ii) zero- and few-shot prompting, (iii) instruction tuning and Retrieval-Augmented Generation (RAG), and (iv) Supervised Fine-Tuning combined with Direct Preference Optimization (DPO) and RAG. Our results reveal clear accuracy-cost-robustness trade-offs across methods, the best configuration, integrating k-SFT and RAG, achieves the strongest overall results with F1-Score 93%. This study offers the first unified empirical comparison of modern LLM-based AES strategies for English L2, promising potential in auto-grading writing tasks. Code is public at https://github.com/MinhNguyenDS/LLM_AES-EnL2 |
| title | From Prompting to Preference Optimization: A Comparative Study of LLM-based Automated Essay Scoring |
| topic | Computation and Language 68T50, 68U10, 68T05 I.2.7; I.2.6; K.3.1 |
| url | https://arxiv.org/abs/2603.06424 |