Effects of Prompt Length on Domain-specific Tasks for Large Language Models
This work addresses the challenge of optimizing prompts for domain-specific applications, which is incremental but relevant for practitioners in fields like finance.
The paper investigates how prompt length affects the performance of large language models on domain-specific tasks like financial sentiment analysis, finding that longer prompts can improve accuracy by up to 15% in certain cases.
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.