# Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model

arXiv: https://arxiv.org/abs/2305.18638 · HTML: https://mlsanity.com/paper/2305.18638/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://mlsanity.com/paper/2010.08240/) (2020)
- [Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers](https://mlsanity.com/paper/2204.03503/) (2022)
- [Leveraging Large Language Model and Story-Based Gamification in Intelligent Tutoring System to Scaffold Introductory Programming Courses: A Design-Based Research Study](https://mlsanity.com/paper/2302.12834/) (2023)

## Built on by (most cited first, 7 of 7)

- [A large language model-assisted education tool to provide feedback on open-ended responses](https://mlsanity.com/paper/2308.02439/) (2023)
- [Grade Like a Human: Rethinking Automated Assessment with Large Language Models](https://mlsanity.com/paper/2405.19694/) (2024)
- [SteLLA: A Structured Grading System Using LLMs with RAG](https://mlsanity.com/paper/2501.09092/) (2025)
- [SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models](https://mlsanity.com/paper/2505.07247/) (2025)
- [Fine-tuning for Better Few Shot Prompting: An Empirical Comparison for Short Answer Grading](https://mlsanity.com/paper/2508.04063/) (2025)
- [Towards Human-Like Grading: A Unified LLM-Enhanced Framework for Subjective Question Evaluation](https://mlsanity.com/paper/2510.07912/) (2025)
- [AI-Enabled grading with near-domain data for scaling feedback with human-level accuracy](https://mlsanity.com/paper/2512.04113/) (2025)

## Abstract (arXiv)

In this study, we developed an automated short answer grading (ASAG) model that provided both analytic scores and final holistic scores. Short answer items typically consist of multiple sub-questions, and providing an analytic score and the text span relevant to each sub-question can increase the interpretability of the automated scores. Furthermore, they can be used to generate actionable feedback for students. Despite these advantages, most studies have focused on predicting only holistic scores due to the difficulty in constructing dataset with manual annotations. To address this difficulty, we used large language model (LLM)-based one-shot prompting and a text similarity scoring model with domain adaptation using small manually annotated dataset. The accuracy and quadratic weighted kappa of our model were 0.67 and 0.71 on a subset of the publicly available ASAG dataset. The model achieved a substantial improvement over the majority baseline.