{"slug": "learning-to-grade-efficiently-a-bandit-driven-prompt-selection-framework-for-low", "title": "Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring", "summary": "Researchers at an undisclosed institution propose a multi-armed bandit (MAB) framework for adaptive prompt selection in automated essay scoring, reducing large language model (LLM) calls by 78.4% while maintaining scoring accuracy comparable to exhaustive grid search. The study, detailed in arXiv:2608.23814v1, tested four grading recipes on IELTS Writing Task 2 essays and found the multi-step approach with calibration examples achieved the highest accuracy, also producing the first cost-reliability learning curves for essay scoring.", "body_md": "arXiv:2608.23814v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/learning-to-grade-efficiently-a-bandit-driven-prompt-selection-framework-for-low", "canonical_source": "https://arxiv.org/abs/2608.23814", "published_at": "2026-08-26 04:00:00+00:00", "updated_at": "2026-08-26 04:13:09.226517+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research"], "entities": ["arXiv", "IELTS Writing Task 2"], "alternates": {"html": "https://wpnews.pro/news/learning-to-grade-efficiently-a-bandit-driven-prompt-selection-framework-for-low", "markdown": "https://wpnews.pro/news/learning-to-grade-efficiently-a-bandit-driven-prompt-selection-framework-for-low.md", "text": "https://wpnews.pro/news/learning-to-grade-efficiently-a-bandit-driven-prompt-selection-framework-for-low.txt", "jsonld": "https://wpnews.pro/news/learning-to-grade-efficiently-a-bandit-driven-prompt-selection-framework-for-low.jsonld"}}