Abstract
In the field of business data analysis, the ability to extract actionable insights from vast and varied datasets is essential for informed decision-making and maintaining a competitive edge. Traditional rule-based systems, while reliable, often fall short when faced with the complexity and dynamism of modern business data. Conversely, Artificial Intelligence (AI) models, particularly Large Language Models (LLMs), offer significant potential in pattern recognition and predictive analytics but can lack the precision necessary for specific business applications. This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of LLMs in generating actionable business insights.
Abstract (translated)
在商务数据分析领域，从庞大的且多样的数据中提取可操作的见解对于明智的决策和保持竞争优势至关重要。虽然传统基于规则的系统是可靠的，但面对现代商业数据的复杂性和动态性时，常常力不从心。相反，人工智能（AI）模型，特别是大型语言模型（LLMs），在模式识别和预测分析方面具有显著潜力，但也可能缺乏特定业务应用程序所需的精度。本文探讨了将基于规则的系统的稳健性与LLM的适应性相结合产生可操作商业见解的混合方法的有效性。
URL
https://arxiv.org/abs/2404.15604
