Error-Supervised Synthetic Learner Writing for Automated Essay Scoring
Synthetic essays can help reduce dependence on human-written data in Automated Essay Scoring (AES). However, they often lack realistic errors, limiting their ability to represent authentic human writing, particularly when the target texts are intended to resemble those produced by language learners. In this study, we present a simple approach that introduces error supervision into synthetic essay generation. Specifically, we fine-tune an LLM generator on error-annotated texts of the kind commonly used in Grammatical Error Detection (GED). To assess the utility of the proposed approach, we fine-tune and evaluate AES scorers under three data conditions: authentic essays, synthetic essays ge...
| Authors | Duy Anh Nguyen |
|---|---|
| Primary category | cs.CL |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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