[2G5-GS-6] Language media processing:

[2G5-GS-6-04] A Better LLM Evaluator for Text Generation: The Impact of Prompt Output Sequencing and Optimization

〇KuanChao Chu¹, Yi-Pei Chen¹, Hideki Nakayama¹ (1. The University of Tokyo)

Keywords:Large Language Models, Prompt, Conversation Evaluation

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 "reason-first" approach yielding more comprehensive evaluations. This insight is crucial for enhancing the accuracy and consistency of LLM-based evaluations.