In recent years, Large Language Models have garnered significant attention for their strong performance in various natural language tasks, such as machine translation and question answering. These models demonstrate an impressive ability to generalize across diverse tasks. However, their effectiveness in tackling domain-specific tasks, such as financial sentiment analysis and monetary policy understanding, remains a topic of debate, as these tasks often require specialized knowledge and precise reasoning. To address such challenges, researchers design various prompts to unlock the models' abilities. By carefully crafting input prompts, researchers can guide these models to produce more accurate responses. Consequently, prompt engineering has become a key focus of study. Despite the advancements in both models and prompt engineering, the relationship between the two-specifically, how prompt design impacts models' ability to perform domain-specific tasks-remains underexplored. This paper aims to bridge this research gap.

本研究旨在解决大语言模型在领域特定任务（如金融情感分析和货币政策理解）中的有效性问题。本文提出了通过设计不同长度的提示来探索其对模型性能的影响，从而填补了现有研究的空白。研究发现，提示设计显著影响模型在特定领域任务中的响应准确度，为后续研究提供了新的思路。

提示长度对大语言模型领域特定任务的影响