# Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education

arXiv: https://arxiv.org/abs/2301.08771 · HTML: https://mlsanity.com/paper/2301.08771/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [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)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://mlsanity.com/paper/2110.07904/) (2021)
- [Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections](https://mlsanity.com/paper/2104.04670/) (2021)
- [On Transferability of Prompt Tuning for Natural Language Processing](https://mlsanity.com/paper/2111.06719/) (2021)
- [Automatic Short Math Answer Grading via In-context Meta-learning](https://mlsanity.com/paper/2205.15219/) (2022)
- [What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes](https://mlsanity.com/paper/1503.06410/) (2015)

## Built on by (most cited first, 18 of 18)

- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [Applying Large Language Models and Chain-of-Thought for Automatic Scoring](https://mlsanity.com/paper/2312.03748/) (2023)
- [Fine-tuning ChatGPT for Automatic Scoring](https://mlsanity.com/paper/2310.10072/) (2023)
- [Using GPT-4 to Augment Unbalanced Data for Automatic Scoring](https://mlsanity.com/paper/2310.18365/) (2023)
- [Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters](https://mlsanity.com/paper/2308.06088/) (2023)
- [Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring](https://mlsanity.com/paper/2407.18328/) (2024)
- [AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition](https://mlsanity.com/paper/2509.21910/) (2025)
- [Ophtha-LLaMA2: A Large Language Model for Ophthalmology](https://mlsanity.com/paper/2312.04906/) (2023)
- [Empirical Study of Large Language Models as Automated Essay Scoring Tools in English Composition__Taking TOEFL Independent Writing Task for Example](https://mlsanity.com/paper/2401.03401/) (2024)
- [AGI: Artificial General Intelligence for Education](https://mlsanity.com/paper/2304.12479/) (2023)
- [Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocols](https://mlsanity.com/paper/2502.12842/) (2025)
- [CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback](https://mlsanity.com/paper/2504.02323/) (2025)
- [Generating Planning Feedback for Open-Ended Programming Exercises with LLMs](https://mlsanity.com/paper/2504.08958/) (2025)
- [AI and Machine Learning for Next Generation Science Assessments](https://mlsanity.com/paper/2405.06660/) (2024)
- [Automated Reading Passage Generation with OpenAI's Large Language Model](https://mlsanity.com/paper/2304.04616/) (2023)
- [G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German](https://mlsanity.com/paper/2402.06584/) (2024)
- [Zero-Shot Classification of Crisis Tweets Using Instruction-Finetuned Large Language Models](https://mlsanity.com/paper/2410.00182/) (2024)
- [Leveraging a Cognitive Model to Measure Subjective Similarity of Human and GPT-4 Written Content](https://mlsanity.com/paper/2409.00269/) (2024)

## Abstract (arXiv)

Developing models to automatically score students' written responses to science problems is critical for science education. However, collecting and labeling sufficient student responses for training models is time and cost-consuming. Recent studies suggest that pre-trained language models can be adapted to downstream tasks without fine-tuning with prompts. However, no research has employed such a prompt approach in science education. As student responses are presented with natural language, aligning the scoring procedure as the next sentence prediction task using prompts can skip the costly fine-tuning stage. In this study, we developed a zero-shot approach to automatically score student responses via Matching Exemplars as Next Sentence Prediction (MeNSP). This approach employs no training samples. We first apply MeNSP in scoring three assessment tasks of scientific argumentation and found machine-human scoring agreements, Cohen's Kappa ranges from 0.30 to 0.57, and F1 score ranges from 0.54 to 0.81. To improve the performance, we extend our research to the few-shots setting, either randomly selecting labeled student responses or manually constructing responses to fine-tune the models. We find that one task's performance is improved with more samples, Cohen's Kappa from 0.30 to 0.38, and F1 score from 0.54 to 0.59; for the two others, scoring performance is not improved. We also find that randomly selected few-shots perform better than the human expert-crafted approach. This study suggests that MeNSP can yield referable automatic scoring for student responses while significantly reducing the cost of model training. This method can benefit low-stakes classroom assessment practices in science education. Future research should further explore the applicability of the MeNSP in different types of assessment tasks in science education and improve the model performance.