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Large Language Models (LLMs) have shown promise in Automated Essay Scoring (AES), but their zero-shot and fewshot performance often falls short compared to state-of-the-art models and human raters.However, fine-tuning LLMs for each specific task is impractical due to the variety of essay prompts and rubrics used in real-world educational contexts.This study proposes a novel approach combining LLMs and Comparative Judgment (CJ) for AES, using zeroshot prompting to choose between two essays.We demonstrate that a CJ method surpasses traditional rubric-based scoring in essay scoring using LLMs.
Kim et al. (Tue,) studied this question.