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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.17247 |
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| _version_ | 1866911691756797952 |
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| author | Yin, Zheqin Ren, Yupei Zhang, Yadong Lu, Yujiang Lan, Man |
| author_facet | Yin, Zheqin Ren, Yupei Zhang, Yadong Lu, Yujiang Lan, Man |
| contents | Argumentative essays serve as a vital medium for assessing critical thinking and reasoning skills, yet there is limited works on accurately understanding and evaluating such texts via prompt. In this work, we propose TIDE, a novel framework designed to improve criteria-based prompt optimization for argument-related tasks by integrating TrIal and DEbate mechanism. Our method addresses key limitations of criteria-based prompt optimizing by mitigating the influence of noisy training data and enhancing optimization stability. We evaluate TIDE on three core tasks: Automated Essay Scoring, Argument Component Detection, and Argument Relation Identification. Results demonstrate that our framework improves performance across tasks. These findings underscore the potential of combining prompt-based methods for advanced argument understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17247 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate Yin, Zheqin Ren, Yupei Zhang, Yadong Lu, Yujiang Lan, Man Artificial Intelligence Argumentative essays serve as a vital medium for assessing critical thinking and reasoning skills, yet there is limited works on accurately understanding and evaluating such texts via prompt. In this work, we propose TIDE, a novel framework designed to improve criteria-based prompt optimization for argument-related tasks by integrating TrIal and DEbate mechanism. Our method addresses key limitations of criteria-based prompt optimizing by mitigating the influence of noisy training data and enhancing optimization stability. We evaluate TIDE on three core tasks: Automated Essay Scoring, Argument Component Detection, and Argument Relation Identification. Results demonstrate that our framework improves performance across tasks. These findings underscore the potential of combining prompt-based methods for advanced argument understanding. |
| title | Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.17247 |