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

• URL: http://arxiv.org/abs/2502.16399v1
• Date: Sun, 23 Feb 2025 01:17:46 GMT
• Title: Ensemble ToT of LLMs and Its Application to Automatic Grading System for Supporting Self-Learning
• Authors: Yuki Ito, Qiang Ma,
• Abstract summary: Ensemble Tree-of-Thought (ToT) is a framework that enhances LLM outputs by integrating multiple models.<n>Our grading system first evaluates the grading tendencies of LLMs, then generates multiple results, and finally integrates them via a simulated debate.
• Score: 0.8490659704051299
• License: http://creativecommons.org/licenses/by/4.0/
• 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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