Hybrid LLM/Rule-based Approaches to Business Insights Generation from Structured Data

• URL: http://arxiv.org/abs/2404.15604v1
• Date: Wed, 24 Apr 2024 02:42:24 GMT
• Title: Hybrid LLM/Rule-based Approaches to Business Insights Generation from Structured Data
• Authors: Aliaksei Vertsel, Mikhail Rumiantsau,
• Abstract summary: 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. This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of Large Language Models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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