CHiL(L)Grader: Calibrated Human-in-the-Loop Short-Answer Grading

• URL: http://arxiv.org/abs/2603.11957v1
• Date: Thu, 12 Mar 2026 14:04:58 GMT
• Title: CHiL(L)Grader: Calibrated Human-in-the-Loop Short-Answer Grading
• Abstract summary: We introduce CHiL(L)Grader, the first automated grading framework that incorporates calibrated confidence estimation into a human-in-the-loop workflow.<n>CHiL(L)Grader automates only high-confidence predictions while routing uncertain cases to human graders, and adapts to evolvings and unseen questions.<n>A QWK gap of 0.347 between accepted and rejected predictions confirms the effectiveness of the confidence-based routing.
• Score: 1.5829605001132387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scaling educational assessment with large language models requires not just accuracy, but the ability to recognize when predictions are trustworthy. Instruction-tuned models tend to be overconfident, and their reliability deteriorates as curricula evolve, making fully autonomous deployment unsafe in high-stakes settings. We introduce CHiL(L)Grader, the first automated grading framework that incorporates calibrated confidence estimation into a human-in-the-loop workflow. Using post-hoc temperature scaling, confidence-based selective prediction, and continual learning, CHiL(L)Grader automates only high-confidence predictions while routing uncertain cases to human graders, and adapts to evolving rubrics and unseen questions. Across three short-answer grading datasets, CHiL(L)Grader automatically scores 35-65% of responses at expert-level quality (QWK >= 0.80). A QWK gap of 0.347 between accepted and rejected predictions confirms the effectiveness of the confidence-based routing. Each correction cycle strengthens the model's grading capability as it learns from teacher feedback. These results show that uncertainty quantification is key for reliable AI-assisted grading.

Related papers

• $TCP_α$: Margin-Controlled Confidence estimation for reliable Music Information Retrieval [7.679660052685777]
  Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions.<n>Existing targets suffer from inherent ambiguity: they assign overlapping confidence values to correct and incorrect predictions, while errors near the decision boundary receive confidence scores indistinguishable from correct predictions.<n>We propose $ TCP_$, a novel confidence target that resolves these limitations by introducing a margin-controlled penalty for misclassified samples.
  arXiv  Detail & Related papers  (2026-08-20T17:58:50Z)
• Can You Trust the Confidence? ConfBench for Vision-Language Models on Document Extraction [13.176724997257802]
  Existing document benchmarks are dominated by clean, high-quality samples.<n>We introduce ConfBench, the first calibration-specific benchmark for key information extraction.<n>We evaluate four proprietary and three open-weight VLMs under verbalized and log-probability confidence estimation methods.
  arXiv  Detail & Related papers  (2026-08-03T07:03:51Z)
• The Illusion of Certainty: Decoupling Capability and Calibration in On-Policy Distillation [67.26315138466312]
  We propose a calibration-aware OPD framework, CaOPD, that estimates empirical confidence from model rollouts.<n>Our findings highlight that capability distillation does not imply confidence, and that confidence should be treated as an essential objective in post-training.
  arXiv  Detail & Related papers  (2026-04-18T04:43:40Z)
• When Can We Trust LLM Graders? Calibrating Confidence for Automated Assessment [1.896929753246251]
  Large Language Models (LLMs) show promise for automated grading, but their outputs can be unreliable.<n>This enables selective automation where high-confidence predictions are processed automatically while uncertain cases are flagged for human review.
  arXiv  Detail & Related papers  (2026-03-31T10:38:22Z)
• On Calibration of Large Language Models: From Response To Capability [66.59139960234326]
  Large language models (LLMs) are widely deployed as general-purpose problem solvers.<n>We introduce capability calibration, which targets the model's expected accuracy on a query.<n>Our results demonstrate that capability-calibrated confidence improves pass@$k$ prediction and inference budget allocation.
  arXiv  Detail & Related papers  (2026-02-14T01:07:45Z)
• Verbalized Confidence Triggers Self-Verification: Emergent Behavior Without Explicit Reasoning Supervision [12.287123198288079]
  Uncertainty calibration is essential for the safe deployment of large language models (LLMs)<n>We find that supervised fine-tuning with scalar confidence labels alone suffices to elicit self-verification behavior of language models.<n>We propose a simple rethinking method that boosts performance via test-time scaling based on calibrated uncertainty.
  arXiv  Detail & Related papers  (2025-06-04T08:56:24Z)
• CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous Driving [0.0]
  End-to-end vision-based imitation learning has demonstrated promising results in autonomous driving.<n>Traditional approaches rely on either regressionbased models, which provide precise control but lack confidence estimation, or classification-based models, which offer confidence scores but suffer from reduced precision due to discretization.<n>We introduce a dual-head neural network architecture that integrates both regression and classification heads to improve decision reliability in imitation learning.
  arXiv  Detail & Related papers  (2025-03-02T08:19:02Z)
• Selective Learning: Towards Robust Calibration with Dynamic Regularization [79.92633587914659]
  Miscalibration in deep learning refers to there is a discrepancy between the predicted confidence and performance. We introduce Dynamic Regularization (DReg) which aims to learn what should be learned during training thereby circumventing the confidence adjusting trade-off.
  arXiv  Detail & Related papers  (2024-02-13T11:25:20Z)
• TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASR [1.8477401359673709]
  Class-probability-based confidence scores do not accurately represent quality of overconfident ASR predictions. We propose a novel Temporal-Lexeme Similarity (TeLeS) confidence score to train Confidence Estimation Model (CEM) We conduct experiments with ASR models trained in three languages, namely Hindi, Tamil, and Kannada, with varying training data sizes.
  arXiv  Detail & Related papers  (2024-01-06T16:29:13Z)
• On the Calibration of Uncertainty Estimation in LiDAR-based Semantic Segmentation [7.100396757261104]
  We propose a metric to measure the confidence calibration quality of a semantic segmentation model with respect to individual classes. We additionally suggest a double use for the method to automatically find label problems to improve the quality of hand- or auto-annotated datasets.
  arXiv  Detail & Related papers  (2023-08-04T10:59:24Z)
• Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback [91.22679548111127]
  A trustworthy real-world prediction system should produce well-calibrated confidence scores. We show that verbalized confidences emitted as output tokens are typically better-calibrated than the model's conditional probabilities.
  arXiv  Detail & Related papers  (2023-05-24T10:12:33Z)
• Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval [51.83967175585896]
  UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
  arXiv  Detail & Related papers  (2022-10-24T17:53:20Z)
• Balancing Cost and Quality: An Exploration of Human-in-the-loop Frameworks for Automated Short Answer Scoring [36.58449231222223]
  Short answer scoring (SAS) is the task of grading short text written by a learner. We present the first study of exploring the use of human-in-the-loop framework for minimizing the grading cost. We find that our human-in-the-loop framework allows automatic scoring models and human graders to achieve the target scoring quality.
  arXiv  Detail & Related papers  (2022-06-16T16:43:18Z)
• An evaluation of word-level confidence estimation for end-to-end automatic speech recognition [70.61280174637913]
  We investigate confidence estimation for end-to-end automatic speech recognition (ASR) We provide an extensive benchmark of popular confidence methods on four well-known speech datasets. Our results suggest a strong baseline can be obtained by scaling the logits by a learnt temperature.
  arXiv  Detail & Related papers  (2021-01-14T09:51:59Z)

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.