Rationale Behind Essay Scores: Enhancing S-LLM’s Multi-Trait Essay Scoring with Rationale Generated by LLMs (2025.findings-naacl)
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| Challenge: | Existing automated essay scoring relies on essay text without explanatory rationales for the scores. |
| Approach: | They propose a rationale-based multiple trait scoring approach that integrates large language models with a smaller large language model to generate trait-specific rationales. |
| Outcome: | The proposed approach outperforms state-of-the-art models and vanilla S-LLMs on benchmark datasets. |
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| Challenge: | Automated essay scoring (AES) is one of the earliest research problems in natural language processing. |
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| Challenge: | Recent advances in automated essay scoring (AES) have relied on labeled essays, requiring tremendous cost and expertise for their acquisition. |
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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu
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| Challenge: | a new framework for automated essay scoring is needed to achieve multi-perspective understanding and judgment. |
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| Challenge: | Existing Automated Essay Scoring (AES) methods focus on sentence-level features, whereas Large Language Models (LLMs) are sensitive to conventions & accuracy, language complexity, and organization. |
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| Challenge: | Existing methods for automatic essay scoring fail to learn trait representations and ignore correlations between trait scores. |
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| Challenge: | Existing systems assume to grade essays on same prompt as used in training and assign only a holistic score. |
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Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards (2024.emnlp-main)
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| Challenge: | Existing reinforcement learning (RL) applications in AES are limited to classification models despite associated performance degradation. |
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