Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring
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Computer Science > Machine Learning
arXiv:2608.23814 (cs)
[Submitted on 24 Aug 2026]
Title:Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring
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Abstract:Large Language Models (LLMs) demonstrate strong capabilities in automated essay scoring (AES), but contemporary approaches typically employ fixed prompt selection, failing to address operational cost concerns and evolving optimal configurations. We propose a novel cost-aware approach that treats each prompt type as an arm in a multi-armed bandit (MAB) controller, enabling adaptive selection of optimal prompting strategies during inference. Our experiments on IELTS Writing Task 2 essays show that the MAB framework achieves comparable scoring accuracy to exhaustive grid search while reducing LLM calls by 78.4\% to find the best grading approach. We implemented four distinct grading recipes (multi-step vs. single-step assessment, with vs. without calibration examples) and found that the multi-step approach with examples achieves the highest accuracy. By tracking token usage and latency alongside agreement metrics, we produce the first cost-reliability learning curves for essay scoring, providing actionable insights for educational technology platforms that must balance operational costs against assessment validity. This work represents the first application of online control mechanisms to adaptively select prompting strategies in AES, transforming prompt selection from an offline hyperparameter optimization problem into an efficient online learning task.
| Comments: | Accepted as a presentation at the EDM 2025 Workshop on Educational Data Mining in Writing and Literacy Instruction |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| MSC classes: | 68T05, 68T50, 68W27 |
| ACM classes: | I.2.6; I.2.7; K.3.1 |
| Cite as: | arXiv:2608.23814 [cs.LG] |
| (or arXiv:2608.23814v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.23814
arXiv-issued DOI via DataCite (pending registration)
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