SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models

Peichao Lai; Kexuan Zhang; Yi Lin; Linyihan Zhang; Feiyang Ye; Jinhao Yan; Yanwei Xu; Conghui He; Yilei Wang; Wentao Zhang; Bin Cui

arXiv · 2505.07247

SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models

Peichao Lai·Kexuan Zhang·Yi Lin·Linyihan Zhang·Feiyang Ye·Jinhao Yan·Yanwei Xu·Conghui He·Yilei Wang·Wentao Zhang·Bin Cui

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Abstract

Subjective Answer Grading (SAG) plays a crucial role in education, standardized testing, and automated assessment systems, particularly for evaluating short-form responses in Short Answer Scoring (SAS). However, existing approaches often produce coarse-grained scores and lack detailed reasoning. Although large language models (LLMs) have demonstrated potential as zero-shot evaluators, they remain susceptible to bias, inconsistencies with human judgment, and limited transparency in scoring decisions. To overcome these limitations, we introduce SAS-Bench, a benchmark specifically designed for LLM-based SAS tasks. SAS-Bench provides fine-grained, step-wise scoring, expert-annotated error categories, and a diverse range of question types derived from real-world subject-specific exams. This benchmark facilitates detailed evaluation of model reasoning processes and explainability. We also release an open-source dataset containing 1,030 questions and 4,109 student responses, each annotated by domain experts. Furthermore, we conduct comprehensive experiments with various LLMs, identifying major challenges in scoring science-related questions and highlighting the effectiveness of few-shot prompting in improving scoring accuracy. Our work offers valuable insights into the development of more robust, fair, and educationally meaningful LLM-based evaluation systems.

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BibTeX RIS

Peichao Lai, Kexuan Zhang, Yi Lin, Linyihan Zhang, Feiyang Ye, Jinhao Yan, Yanwei Xu, Conghui He, Yilei Wang, Wentao Zhang, Bin Cui. 2025-05-15. SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models. https://arxiv.org/abs/2505.07247

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