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

• URL: http://arxiv.org/abs/2505.07247v2
• Date: Thu, 15 May 2025 11:01:45 GMT
• Title: SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models
• Authors: Peichao Lai, Kexuan Zhang, Yi Lin, Linyihan Zhang, Feiyang Ye, Jinhao Yan, Yanwei Xu, Conghui He, Yilei Wang, Wentao Zhang, Bin Cui,
• Abstract summary: SAS-Bench is a benchmark for large language models (LLMs) based Short Answer Scoring tasks.<n>It provides fine-grained, step-wise scoring, expert-annotated error categories, and a diverse range of question types.<n>We also release an open-source dataset containing 1,030 questions and 4,109 student responses, each annotated by domain experts.
• Score: 36.10798324093408
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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