Effects of Prompt Length on Domain-specific Tasks for Large Language Models

TL;DR

This paper investigates how the length and design of prompts influence large language models' performance on domain-specific tasks, highlighting the importance of prompt engineering in specialized applications.

Contribution

It explores the impact of prompt length on model effectiveness in domain-specific tasks, addressing a gap in understanding prompt design's role in model performance.

Findings

Prompt length significantly affects task accuracy.

Optimal prompt design improves domain-specific task performance.

Longer prompts do not always lead to better results.

Abstract

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.