@inproceedings{603621f41966490ab64bcca2e4a488a5,
title = "ASAGeR: Automated Short Answer Grading Regressor via Sentence Simplification",
abstract = "We propose an automated short answer grading system (ASAG) to estimate the student answer scores via text summarization from LLMs. The step of text summarization provides enough question answer normalization so that the summarized answers have the answer keys well organized and the grading based on that should be more accurate and easier than before, no matter the answers are graded by human or automatic graders. On the other hand, we also discuss the scenario when more than one grader are involved in the grading but providing inconsistent scores. We adopt a majority voting mechanism to overcome such difficulty and produce superior result in average. Overall the proposed methodology has its evaluation done to show the superiority to other state-of-the-art methods. The pre-trained transformer version 3.5 (GPT 3.5) is used to serve the text summarization tool given a well-designed prompt.",
keywords = "Answer Grading, Natural Language Processing, Regression, Text Simplification",
author = "Mohammad Iqbal and {Laili Udhiah}, Rosita and {Rana Nugraha}, Tsamarah and Pao, {Hsing Kuo}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th IEEE International Conference on Knowledge Graph, ICKG 2023 ; Conference date: 01-12-2023 Through 02-12-2023",
year = "2023",
doi = "10.1109/ICKG59574.2023.00013",
language = "English",
series = "Proceedings - IEEE International Conference on Knowledge Graph, ICKG 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "60--68",
editor = "Sheng, {Victor S.} and Chindo Hicks and Charles Ling and Vijay Raghavan and Xindong Wu",
booktitle = "Proceedings - IEEE International Conference on Knowledge Graph, ICKG 2023",
address = "United States",
}