# Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay Scoring

arXiv: https://arxiv.org/abs/2008.01441 · HTML: https://mlsanity.com/paper/2008.01441/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 22 of 22)

- [Human-AI Collaborative Essay Scoring: A Dual-Process Framework with LLMs](https://mlsanity.com/paper/2401.06431/) (2024)
- [Unleashing Large Language Models' Proficiency in Zero-shot Essay Scoring](https://mlsanity.com/paper/2404.04941/) (2024)
- [Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring](https://mlsanity.com/paper/2305.16826/) (2023)
- [AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition](https://mlsanity.com/paper/2509.21910/) (2025)
- [TRATES: Trait-Specific Rubric-Assisted Cross-Prompt Essay Scoring](https://mlsanity.com/paper/2505.14577/) (2025)
- [Unveiling the Tapestry of Automated Essay Scoring: A Comprehensive Investigation of Accuracy, Fairness, and Generalizability](https://mlsanity.com/paper/2401.05655/) (2024)
- [Improve LLM-based Automatic Essay Scoring with Linguistic Features](https://mlsanity.com/paper/2502.09497/) (2025)
- [LCES: Zero-shot Automated Essay Scoring via Pairwise Comparisons Using Large Language Models](https://mlsanity.com/paper/2505.08498/) (2025)
- [Rank-Then-Score: Enhancing Large Language Models for Automated Essay Scoring](https://mlsanity.com/paper/2504.05736/) (2025)
- [Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset](https://mlsanity.com/paper/2310.18373/) (2023)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German](https://mlsanity.com/paper/2402.06584/) (2024)
- [Activations as Features: Probing LLMs for Generalizable Essay Scoring Representations](https://mlsanity.com/paper/2512.19456/) (2025)
- [Review of feedback in Automated Essay Scoring](https://mlsanity.com/paper/2307.05553/) (2023)
- [Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring](https://mlsanity.com/paper/2502.08450/) (2025)
- [SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models](https://mlsanity.com/paper/2505.07247/) (2025)
- [Composable Cross-prompt Essay Scoring by Merging Models](https://mlsanity.com/paper/2505.18548/) (2025)
- [Multilingual Embedding Probes Fail to Generalize Across Learner Corpora](https://mlsanity.com/paper/2604.07095/) (2026)
- [MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring](https://mlsanity.com/paper/2604.17569/) (2026)
- [The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring](https://mlsanity.com/paper/2606.10327/) (2026)
- [Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering](https://mlsanity.com/paper/2606.12422/) (2026)
- [From Texts to Scores: Tracing the Emergence of Essay Quality Representations in Large Language Models](https://mlsanity.com/paper/2606.20152/) (2026)

## Abstract (arXiv)

Cross-prompt automated essay scoring (AES) requires the system to use non target-prompt essays to award scores to a target-prompt essay. Since obtaining a large quantity of pre-graded essays to a particular prompt is often difficult and unrealistic, the task of cross-prompt AES is vital for the development of real-world AES systems, yet it remains an under-explored area of research. Models designed for prompt-specific AES rely heavily on prompt-specific knowledge and perform poorly in the cross-prompt setting, whereas current approaches to cross-prompt AES either require a certain quantity of labelled target-prompt essays or require a large quantity of unlabelled target-prompt essays to perform transfer learning in a multi-step manner. To address these issues, we introduce Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our method requires no access to labelled or unlabelled target-prompt data during training and is a single-stage approach. PAES is easy to apply in practice and achieves state-of-the-art performance on the Automated Student Assessment Prize (ASAP) dataset.