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

Business

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

A. Vertsel and M. Rumiantsau

This paper, conducted by Aliaksei Vertsel and Mikhail Rumiantsau, delves into innovative hybrid approaches that merge rule-based systems and Large Language Models (LLMs) to extract actionable business insights from complex datasets. It highlights how this combination can overcome the limitations faced by traditional systems and standalone models, promising enhanced data extraction and insights generation.

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Playback language: English
Abstract
This paper explores hybrid approaches integrating rule-based systems and Large Language Models (LLMs) for generating actionable business insights from complex datasets. It addresses the limitations of traditional rule-based systems and standalone LLMs, proposing a solution that combines their respective strengths for enhanced data extraction and insight generation.
Publisher
Published On
Authors
Aliaksei Vertsel, Mikhail Rumiantsau
Tags
rule-based systems
Large Language Models
business insights
data extraction
hybrid approaches
actionable insights
complex datasets

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