LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias

• URL: http://arxiv.org/abs/2604.00259v1
• Date: Tue, 31 Mar 2026 21:40:36 GMT
• Title: LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias
• Abstract summary: We analyze agreement with human consensus scores, directional bias, and the stability of bias estimates.<n>We observe large and stable negative directional bias on Lower-Order Concern (LOC) traits, such as Grammar and Conventions.<n>This analysis shows that LOC bias is often detectable with very small validation sets.
• Score: 3.9562034587217187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring. We present a systematic evaluation of instruction-tuned LLMs across three open essay-scoring datasets (ASAP 2.0, ELLIPSE, and DREsS) that cover both holistic and analytic scoring. We analyze agreement with human consensus scores, directional bias, and the stability of bias estimates. Our results show that strong open-weight models achieve moderate to high agreement with humans on holistic scoring (Quadratic Weighted Kappa about 0.6), but this does not transfer uniformly to analytic scoring. In particular, we observe large and stable negative directional bias on Lower-Order Concern (LOC) traits, such as Grammar and Conventions, meaning that models often score these traits more harshly than human raters. We also find that concise keyword-based prompts generally outperform longer rubric-style prompts in multi-trait analytic scoring. To quantify the amount of data needed to detect these systematic deviations, we compute the minimum sample size at which a 95% bootstrap confidence interval for the mean bias excludes zero. This analysis shows that LOC bias is often detectable with very small validation sets, whereas Higher-Order Concern (HOC) traits typically require much larger samples. These findings support a bias-correction-first deployment strategy: instead of relying on raw zero-shot scores, systematic score offsets can be estimated and corrected using small human-labeled bias-estimation sets, without requiring large-scale fine-tuning.

Related papers

• Reference-Free Rating of LLM Responses via Latent Information [53.463883683503106]
  We study the common practice of asking a judge model to assign Likert-scale scores to free-text responses.<n>We then propose and evaluate Latent Judges, which derive scalar ratings from internal model signals.<n>Across a broad suite of pairwise and single-rating benchmarks, latent methods match or surpass standard prompting.
  arXiv  Detail & Related papers  (2025-09-29T12:15:52Z)
• Flattery, Fluff, and Fog: Diagnosing and Mitigating Idiosyncratic Biases in Preference Models [12.445845925904466]
  Language models serve as proxies for human preference judgements in alignment and evaluation.<n>They exhibit systematic miscalibration, prioritizing superficial patterns over substantive qualities.<n>This bias manifests as overreliance on features like length, structure, and style, leading to issues like reward hacking and unreliable evaluations.
  arXiv  Detail & Related papers  (2025-06-05T17:59:32Z)
• T2I-Eval-R1: Reinforcement Learning-Driven Reasoning for Interpretable Text-to-Image Evaluation [60.620408007636016]
  We propose T2I-Eval-R1, a novel reinforcement learning framework that trains open-source MLLMs using only coarse-grained quality scores.<n>Our approach integrates Group Relative Policy Optimization into the instruction-tuning process, enabling models to generate both scalar scores and interpretable reasoning chains.
  arXiv  Detail & Related papers  (2025-05-23T13:44:59Z)
• ShortcutProbe: Probing Prediction Shortcuts for Learning Robust Models [26.544938760265136]
  Deep learning models inadvertently learn spurious correlations between targets and non-essential features.<n>In this paper, we propose a novel post hoc spurious bias mitigation framework without requiring group labels.<n>Our framework, termed ShortcutProbe, identifies prediction shortcuts that reflect potential non-robustness in predictions in a given model's latent space.
  arXiv  Detail & Related papers  (2025-05-20T04:21:17Z)
• Class-Conditional Distribution Balancing for Group Robust Classification [11.525201208566925]
  Spurious correlations that lead models to correct predictions for the wrong reasons pose a critical challenge for robust real-world generalization.<n>We offer a novel perspective by reframing the spurious correlations as imbalances or mismatches in class-conditional distributions.<n>We propose a simple yet effective robust learning method that eliminates the need for both bias annotations and predictions.
  arXiv  Detail & Related papers  (2025-04-24T07:15:53Z)
• Reducing Biases in Record Matching Through Scores Calibration [1.5530839016602822]
  We propose a threshold-independent framework for measuring and reducing score bias.<n>We show that several state-of-the-art matching methods exhibit substantial score bias, even when appearing fair under standard threshold-based metrics.<n>We introduce two post-processing score calibration algorithms. The first, calib, aligns group-wise score distributions using the Wasserstein barycenter, targeting demographic parity.<n>The second, ccalib, conditions on predicted labels to further reduce label-dependent biases, such as equal opportunity.
  arXiv  Detail & Related papers  (2024-11-03T21:01:40Z)
• Semi-supervised Learning For Robust Speech Evaluation [30.593420641501968]
  Speech evaluation measures a learners oral proficiency using automatic models. This paper proposes to address such challenges by exploiting semi-supervised pre-training and objective regularization. An anchor model is trained using pseudo labels to predict the correctness of pronunciation.
  arXiv  Detail & Related papers  (2024-09-23T02:11:24Z)
• Self-supervised debiasing using low rank regularization [59.84695042540525]
  Spurious correlations can cause strong biases in deep neural networks, impairing generalization ability. We propose a self-supervised debiasing framework potentially compatible with unlabeled samples. Remarkably, the proposed debiasing framework significantly improves the generalization performance of self-supervised learning baselines.
  arXiv  Detail & Related papers  (2022-10-11T08:26:19Z)
• Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation [44.319739489968164]
  Deep neural networks often take dataset biases as a shortcut to make decisions rather than understand tasks. In this study, we focus on the spurious correlation between word features and labels that models learn from the biased data distribution. We propose a training strategy Less-Learn-Shortcut (LLS): our strategy quantifies the biased degree of the biased examples and down-weights them accordingly.
  arXiv  Detail & Related papers  (2022-05-25T09:08:35Z)
• General Greedy De-bias Learning [163.65789778416172]
  We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
  arXiv  Detail & Related papers  (2021-12-20T14:47:32Z)
• Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases [55.45617404586874]
  We propose a few-shot instruction-based method for prompting pre-trained language models (LMs) We show that large LMs can detect different types of fine-grained biases with similar and sometimes superior accuracy to fine-tuned models.
  arXiv  Detail & Related papers  (2021-12-15T04:19:52Z)
• An Investigation of Why Overparameterization Exacerbates Spurious Correlations [98.3066727301239]
  We identify two key properties of the training data that drive this behavior. We show how the inductive bias of models towards "memorizing" fewer examples can cause over parameterization to hurt.
  arXiv  Detail & Related papers  (2020-05-09T01:59:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.