探索用于联合作文打分与反馈生成的LLM提示策略
Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation
AI总结：
本文探讨了基于LLM的零样本和少样本作文反馈生成提示策略，发现联合处理作文打分与反馈生成可提升打分性能，但打分对反馈质量影响较低。
摘要：
个性化反馈能帮助学生提高作文写作技巧。然而，提供此类反馈所需的人工精力限制了实际中的个性化。自动生成的作文反馈可作为一种替代方案，让学生按照自己的节奏、便利和期望频率进行学习。大型语言模型（LLMs）在生成连贯且与上下文相关的文本方面表现出色。但是，它们提供有用作文反馈的能力尚不明确。本文探讨了基于LLM的零样本和少样本作文反馈生成的几种提示策略。受Chain-of-Thought提示启发，我们研究了自动作文评分（AES）能在多大程度上使生成反馈的质量受益。我们评估了LLM仅通过提示即可达到的AES性能，以及所生成作文反馈的有用性。结果表明，联合处理AES和反馈生成能提升AES性能。然而，尽管我们的人工评估强调了所生成作文反馈的质量，但作文打分对生成反馈的最终影响仍然较低。
英文摘要：
Individual feedback can help students improve their essay writing skills. However, the manual effort required to provide such feedback limits individualization in practice. Automatically-generated essay feedback may serve as an alternative to guide students at their own pace, convenience, and desired frequency. Large language models (LLMs) have demonstrated strong performance in generating coherent and contextually relevant text. Yet, their ability to provide helpful essay feedback is unclear. This work explores several prompting strategies for LLM-based zero-shot and few-shot generation of essay feedback. Inspired by Chain-of-Thought prompting, we study how and to what extent automated essay scoring (AES) can benefit the quality of generated feedback. We evaluate both the AES performance that LLMs can achieve with prompting only and the helpfulness of the generated essay feedback. Our results suggest that tackling AES and feedback generation jointly improves AES performance. However, while our manual evaluation emphasizes the quality of the generated essay feedback, the impact of essay scoring on the generated feedback remains low ultimately.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
