Sensitivity and Robustness of Large Language Models to Prompt Template in Japanese Text Classification Tasks

TL;DR

This paper evaluates the sensitivity and robustness of large language models, especially GPT-4, to prompt template variations in Japanese text classification, revealing significant stability issues and performance drops.

Contribution

It provides a comprehensive analysis of how prompt template modifications affect LLM performance in Japanese, highlighting stability issues in current models like GPT-4.

Findings

GPT-4 accuracy drops from 49.21% to 25.44% with prompt variation

Large language models show significant sensitivity to prompt structure in Japanese

Current models face stability challenges in multilingual prompt tasks

Abstract

Prompt engineering relevance research has seen a notable surge in recent years, primarily driven by advancements in pre-trained language models and large language models. However, a critical issue has been identified within this domain: the inadequate of sensitivity and robustness of these models towards Prompt Templates, particularly in lesser-studied languages such as Japanese. This paper explores this issue through a comprehensive evaluation of several representative Large Language Models (LLMs) and a widely-utilized pre-trained model(PLM). These models are scrutinized using a benchmark dataset in Japanese, with the aim to assess and analyze the performance of the current multilingual models in this context. Our experimental results reveal startling discrepancies. A simple modification in the sentence structure of the Prompt Template led to a drastic drop in the accuracy of GPT-4 from 49.21 to 25.44. This observation underscores the fact that even the highly performance GPT-4 model encounters significant stability issues when dealing with diverse Japanese prompt templates, rendering the consistency of the model's output results questionable. In light of these findings, we conclude by proposing potential research trajectories to further enhance the development and performance of Large Language Models in their current stage.