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

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arxiv 2008.01441 v1 pith:PWBRP4IR submitted 2020-08-04 cs.CL cs.AIcs.LG

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

Robert Ridley , Liang He , Xinyu Dai , Shujian Huang , Jiajun Chen This is my paper
classification cs.CL cs.AIcs.LG
keywords cross-promptessaytarget-promptessaysautomatedpromptquantityagnostic
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Texts to Scores: Tracing the Emergence of Essay Quality Representations in Large Language Models

    cs.CL 2026-06 unverdicted novelty 6.0

    LLM representations encode essay quality in a linearly decodable form that emerges across layers and includes identifiable scoring neurons whose distribution shifts with essay length.

  2. The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring

    cs.CL 2026-06 unverdicted novelty 5.0

    Sequential fine-tuning of LLaMA-3.1-8B on discourse elements in order outperforms independent and randomized curricula for AES on PERSUADE 2.0, with specific F1/accuracy gains and competitiveness vs. LLaMA-70B on conc...

  3. MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring

    cs.CL 2026-04 unverdicted novelty 5.0

    MAPLE uses meta-learning with prototypical networks to learn transferable representations and achieves state-of-the-art cross-prompt essay scoring on ELLIPSE, LAILA, and parts of ASAP datasets.