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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

Robert Ridley , Liang He , Xinyu Dai , Shujian Huang , Jiajun Chen This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords cross-promptessaytarget-promptessaysautomatedpromptquantityagnostic
verification ladder T0 review T1 audit T2 compute T3 formal
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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 5 Pith papers

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

  1. Adversarial Topic-aware Prompt-tuning for Cross-topic Automated Essay Scoring

    cs.CL 2025-08 reject novelty 6.0 of 10

    ATOP uses shared and topic-specific soft prompts with adversarial training and pseudo-labels to improve cross-topic automated essay scoring, reporting better QWK scores than nine baselines on ASAP++.

  2. Composable Cross-prompt Essay Scoring by Merging Models

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Merging LoRA adapters with Bayesian-optimized weights guided by a prior-encoded information maximization objective enables source-free cross-prompt essay scoring.

  3. TRATES: Trait-Specific Rubric-Assisted Cross-Prompt Essay Scoring

    cs.CL 2025-05 conditional novelty 6.0 of 10

    TRATES generates rubric-based trait-specific questions with an LLM, extracts high/medium/low answers as features, and trains a regression model that outperforms prior cross-prompt trait scorers.

  4. Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Using grammar-corrected essays as a second input improves cross-prompt trait scoring, with the largest gains on grammar-related traits like Conventions.

  5. Improve LLM-based Automatic Essay Scoring with Linguistic Features

    cs.CL 2025-02 conditional novelty 4.0 of 10

    Adding handcrafted linguistic features to zero-shot LLM prompts modestly improves automatic essay scoring for Mistral-7B on ASAP and ELLIPSE, but GPT-4 shows no benefit on ASAP and no significance tests are provided.

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