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LLM essay scoring framework cuts costs by 78% using bandit approach

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for efficient Large Language Model (LLM) essay scoring, utilizing a multi-armed bandit (MAB) approach to adaptively select optimal prompting strategies. This method significantly reduces the number of LLM calls required, achieving comparable scoring accuracy to exhaustive methods while cutting costs by over 78%. The study also introduced cost-reliability learning curves, offering valuable insights for educational technology platforms balancing operational expenses with assessment validity. AI

IMPACT This research offers a more cost-effective method for automated essay scoring, potentially lowering the barrier for educational platforms to implement LLM-based assessments.

RANK_REASON Academic paper detailing a novel method for LLM application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM essay scoring framework cuts costs by 78% using bandit approach

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43 / 100
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Academic paper detailing a novel method for LLM application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Olga Manakina, Igor Bogdanov ·

    Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring

    arXiv:2608.23814v1 Announce Type: cross 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 …

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