This research investigates prompt designs of evaluating generated texts using large language models (LLMs). While LLMs are increasingly used for scoring various inputs, creating effective prompts for open-ended text evaluation remains challenging due to model sensitivity and subjectivity in evaluation of text generation. Our study experimented with different prompt structures, altering the sequence of output instructions and including explanatory reasons. We found that the order of presenting reasons and scores significantly influences LLMs' scoring, with a different level of rule understanding in the prompt. An additional optimization may enhance scoring alignment if sufficient data is available. This insight is crucial for improving the accuracy and consistency of LLM-based evaluations.
@article{arxiv.2406.09972,
title = {A Better LLM Evaluator for Text Generation: The Impact of Prompt Output Sequencing and Optimization},
author = {KuanChao Chu and Yi-Pei Chen and Hideki Nakayama},
journal= {arXiv preprint arXiv:2406.09972},
year = {2024}
}
Comments
Presented in JSAI 2024. The first two authors contributed equally. arXiv admin note: substantial text overlap with arXiv:2406.02863