CAMS: Collaborative Small-Parameter Large Language Models for Educational QA Grading

Automated grading has become a crucial component of smart education, involving various complex Natural Language Processing NLP tasks, including text representation, similarity evaluation, and classification. Although large language models LLMs show great promise in improving grading accuracy and consistency, their high computational costs and data privacy concerns limit widespread adoption. This study introduces CAMS, an automated grading system based on smaller LLMs that enhances the grading process through model collaboration and chain-of-thought CoT -guided prompt templates. CAMS offers an efficient, locally deployable, and sustainable solution. By integrating Yi-1.5-9B into the proposed collaborative CAMS system and deploying it locally, the system achieved a grading score of 0.8511 and an overall score of 0.8148, demonstrating improvements of 0.1865 and 0.1732, respectively, compared to the standalone use of Yi-1.5-9B. Furthermore, the performance of CAMS approaches that of larger-scale model APIs such as GPT-3.5-Turbo score = 0.8457 .