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

2025-02-20

AI Summary

Large language models (LLMs) face challenges in domain-specific tasks—such as financial sentiment analysis and monetary policy interpretation—due to insufficient domain knowledge activation and limited reasoning accuracy; the relationship between prompt length and model performance remains poorly characterized. This work systematically quantifies the marginal effect of prompt length on professional-domain tasks for the first time, conducting controlled experiments across six financial and legal benchmark datasets using open-source models (e.g., LLaMA, Qwen), augmented by attention visualization and token-level gradient attribution analysis. Results reveal a significant nonlinear relationship between prompt length and performance: excessively long prompts degrade domain-specific reasoning accuracy—up to a 11.7% drop in peak accuracy. We propose a length-adaptive prompt truncation strategy that, when applied at optimal lengths, yields an average F1-score improvement of 3.2%. Our findings provide an interpretable, reusable methodology for prompt engineering in professional-domain applications.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Impact of prompt length on LLMs
• Domain-specific task performance
• Prompt engineering for specialized knowledge

Innovation

• Investigates prompt length impact
• Explores domain-specific task performance
• Focuses on prompt engineering techniques