Current research on generative language models (GLMs) for automated text scoring (ATS) has focused almost exclusively on querying proprietary models via Application Programming Interfaces (APIs). Yet such practices raise issues around transparency and security, and these methods offer little in the way of efficiency or customizability. With the recent proliferation of smaller, open-source models, there is the option to explore GLMs with computers equipped with modest, consumer-grade hardware, that is, for the "GPU poor." In this study, we analyze the performance and efficiency of open-source, small-scale GLMs for ATS. Results show that GLMs can be fine-tuned to achieve adequate, though not state-of-the-art, performance. In addition to ATS, we take small steps towards analyzing models' capacity for generating feedback by prompting GLMs to explain their scores. Model-generated feedback shows promise, but requires more rigorous evaluation focused on targeted use cases.

通过分析开源的小型生成语言模型（GLMs）在自动化文本评分（ATS）中的性能和效率，结果表明GLMs可以进行适当的微调以达到足够的性能。此外，在分析模型生成反馈能力方面，虽然由模型生成的反馈有潜力，但需要更严格的针对特定使用情况的评估。

基于生成型AI的自动文本评分技术在性能较低的GPU环境中的应用