TRATES: Trait-Specific Rubric-Assisted Cross-Prompt Essay Scoring
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913868525076480 |
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| author | Eltanbouly, Sohaila Albatarni, Salam Elsayed, Tamer |
| author_facet | Eltanbouly, Sohaila Albatarni, Salam Elsayed, Tamer |
| contents | Research on holistic Automated Essay Scoring (AES) is long-dated; yet, there is a notable lack of attention for assessing essays according to individual traits. In this work, we propose TRATES, a novel trait-specific and rubric-based cross-prompt AES framework that is generic yet specific to the underlying trait. The framework leverages a Large Language Model (LLM) that utilizes the trait grading rubrics to generate trait-specific features (represented by assessment questions), then assesses those features given an essay. The trait-specific features are eventually combined with generic writing-quality and prompt-specific features to train a simple classical regression model that predicts trait scores of essays from an unseen prompt. Experiments show that TRATES achieves a new state-of-the-art performance across all traits on a widely-used dataset, with the generated LLM-based features being the most significant. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TRATES: Trait-Specific Rubric-Assisted Cross-Prompt Essay Scoring Eltanbouly, Sohaila Albatarni, Salam Elsayed, Tamer Computation and Language Research on holistic Automated Essay Scoring (AES) is long-dated; yet, there is a notable lack of attention for assessing essays according to individual traits. In this work, we propose TRATES, a novel trait-specific and rubric-based cross-prompt AES framework that is generic yet specific to the underlying trait. The framework leverages a Large Language Model (LLM) that utilizes the trait grading rubrics to generate trait-specific features (represented by assessment questions), then assesses those features given an essay. The trait-specific features are eventually combined with generic writing-quality and prompt-specific features to train a simple classical regression model that predicts trait scores of essays from an unseen prompt. Experiments show that TRATES achieves a new state-of-the-art performance across all traits on a widely-used dataset, with the generated LLM-based features being the most significant. |
| title | TRATES: Trait-Specific Rubric-Assisted Cross-Prompt Essay Scoring |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.14577 |