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.

通过探索多种提词策略，本研究旨在研究基于大型语言模型（LLMs）的零样本和少样本生成文章反馈的能力，发现同时处理自动化文章评分（AES）和反馈生成可以改善 AES 性能，但评分对生成的反馈质量的影响最终仍较低。