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arXiv · 2502.16399

Ensemble ToT of LLMs and Its Application to Automatic Grading System for Supporting Self-Learning

Yuki Ito·Qiang Ma
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Abstract

Providing students with detailed and timely grading feedback is essential for self-learning. While existing LLM-based grading systems are promising, most of them rely on one single model, which limits their performance. To address this, we propose Ensemble Tree-of-Thought (ToT), a framework that enhances LLM outputs by integrating multiple models. Using this framework, we develop a grading system. Ensemble ToT follows three steps: (1) analyzing LLM performance, (2) generating candidate answers, and (3) refining them into a final result. Based on this, our grading system first evaluates the grading tendencies of LLMs, then generates multiple results, and finally integrates them via a simulated debate. Experimental results demonstrate our approach's ability to provide accurate and explainable grading by effectively coordinating multiple LLMs.

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Yuki Ito, Qiang Ma. 2025-02-23. Ensemble ToT of LLMs and Its Application to Automatic Grading System for Supporting Self-Learning. https://arxiv.org/abs/2502.16399

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