Automatic grading and feedback have been long studied using traditional machine learning and deep learning techniques using language models. With the recent accessibility to high performing large language models (LLMs) like LLaMA-2, there is an opportunity to investigate the use of these LLMs for automatic grading and feedback generation. Despite the increase in performance, LLMs require significant computational resources for fine-tuning and additional specific adjustments to enhance their performance for such tasks. To address these issues, Parameter Efficient Fine-tuning (PEFT) methods, such as LoRA and QLoRA, have been adopted to decrease memory and computational requirements in model fine-tuning. This paper explores the efficacy of PEFT-based quantized models, employing classification or regression head, to fine-tune LLMs for automatically assigning continuous numerical grades to short answers and essays, as well as generating corresponding feedback. We conducted experiments on both proprietary and open-source datasets for our tasks. The results show that prediction of grade scores via finetuned LLMs are highly accurate, achieving less than 3% error in grade percentage on average. For providing graded feedback fine-tuned 4-bit quantized LLaMA-2 13B models outperform competitive base models and achieve high similarity with subject matter expert feedback in terms of high BLEU and ROUGE scores and qualitatively in terms of feedback. The findings from this study provide important insights into the impacts of the emerging capabilities of using quantization approaches to fine-tune LLMs for various downstream tasks, such as automatic short answer scoring and feedback generation at comparatively lower costs and latency.

翻译：自动评分与反馈长期以来一直是通过传统机器学习与深度学习技术,借助语言模型展开研究。随着高性能大型语言模型(如LLaMA-2)的普及,如今有机会探究如何利用这些大语言模型进行自动评分和生成反馈。尽管性能有所提升,但大语言模型在微调时需要大量计算资源,并需进行额外的专门调整才能增强其在这类任务中的表现。为应对这些挑战,参数高效微调方法(如LoRA和QLoRA)被采用,以降低模型微调中的内存和计算需求。本文探讨了基于参数高效微调的量化模型(采用分类或回归头部)在微调大语言模型方面的效果,旨在自动为短回答和论文分配连续性数值评分,同时生成相应的反馈。我们在专有数据集和开源数据集上对任务进行了实验。结果表明,通过微调的大语言模型预测的评分分数高度准确,平均评分百分比误差低于3%。在提供分级反馈方面,微调后的4位量化LLaMA-2 13B模型优于竞争基础模型,并且在BLEU和ROUGE分数以及反馈质量方面,与领域专家反馈具有高度相似性。本研究的发现为量化方法微调大语言模型用于各类下游任务(如以较低成本和延迟实现自动短回答评分与反馈生成)的新兴能力影响提供了重要见解。