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This study aims to develop a framework for automating the evaluation of natural language texts based on multiple assessment dimensions.

January 1, 2024Open Access

LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts

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This study aims to develop a framework for automating the evaluation of natural language texts based on multiple assessment dimensions.
Developed a rubric to evaluate texts across various dimensions.
Used a large language model to generate predictions based on rubric questions.
Trained a feed-forward neural network to combine LLM outputs and predict human judges' assessments.
Predicted overall user satisfaction effectively, with RMS error of 0.5, a 2-point improvement over baseline.
Combined LLM predictions showed significant agreement with human judgments on text quality.
Assessment dimensions included naturalness, conciseness, and citation quality.

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Randomized trial assesses text quality in dialogue systems, suggesting improved AI evaluation methods.

Hashemi et al. (2024) studied this question.

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