Towards Human-Like Grading: A Unified LLM-Enhanced Framework for Subjective Question Evaluation

• URL: http://arxiv.org/abs/2510.07912v1
• Date: Thu, 09 Oct 2025 08:05:39 GMT
• Title: Towards Human-Like Grading: A Unified LLM-Enhanced Framework for Subjective Question Evaluation
• Authors: Fanwei Zhua, Jiaxuan He, Xiaoxiao Chen, Zulong Chen, Quan Lu, Chenrui Mei,
• Abstract summary: We propose a unified Large Language Model (LLM)-enhanced auto-grading framework that provides human-like evaluation for all types of subjective questions.<n>Our framework integrates four complementary modules to holistically evaluate student answers.
• Score: 11.709100855086291
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic grading of subjective questions remains a significant challenge in examination assessment due to the diversity in question formats and the open-ended nature of student responses. Existing works primarily focus on a specific type of subjective question and lack the generality to support comprehensive exams that contain diverse question types. In this paper, we propose a unified Large Language Model (LLM)-enhanced auto-grading framework that provides human-like evaluation for all types of subjective questions across various domains. Our framework integrates four complementary modules to holistically evaluate student answers. In addition to a basic text matching module that provides a foundational assessment of content similarity, we leverage the powerful reasoning and generative capabilities of LLMs to: (1) compare key knowledge points extracted from both student and reference answers, (2) generate a pseudo-question from the student answer to assess its relevance to the original question, and (3) simulate human evaluation by identifying content-related and non-content strengths and weaknesses. Extensive experiments on both general-purpose and domain-specific datasets show that our framework consistently outperforms traditional and LLM-based baselines across multiple grading metrics. Moreover, the proposed system has been successfully deployed in real-world training and certification exams at a major e-commerce enterprise.

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