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

The increasing complexity and volume of business data necessitate advanced analytical techniques. Traditional rule-based systems struggle with the intricacies of modern data, while standalone AI models, such as LLMs, may lack the precision required for specific business applications. This paper introduces a hybrid approach that leverages the strengths of both rule-based systems and LLMs to overcome these limitations. The core research question is whether a hybrid approach offers superior performance compared to solely rule-based or LLM-based approaches in extracting and generating actionable business insights from structured data. The context is the growing need for effective business intelligence in an increasingly data-rich environment. The purpose of this study is to investigate the effectiveness of a hybrid model that combines the accuracy and interpretability of rule-based systems with the adaptive learning and natural language generation capabilities of LLMs. The importance lies in improving the efficiency and accuracy of business data analysis, ultimately leading to better-informed decision-making and a competitive advantage.

The paper references several studies, including work on visualizing the interpretation of criteria-driven systems for evaluating health news quality, deconfounding user satisfaction estimation from response rate bias, and various arXiv papers focusing on LLMs and knowledge graphs for question answering, SQL-to-text generation, LLMs in HCI data work, LLMs leveraging graph structural information, a hybrid approach to aspect-based sentiment analysis, enhancing legal document retrieval with LLMs, ChainLM for improved chain-of-thought prompting, and patents on facilitating provisioning contexts for business situations using semantic graphs and generating actionable insight information from datasets using an AI-based natural language interface. These references highlight existing research on various aspects of AI-driven data analysis and natural language processing, setting the stage for the proposed hybrid approach.

The paper outlines a hybrid LLM/rule-based approach combining interpretable AI techniques (like LIME), rule-based systems, and supervised document classification. The LLM integrates with rule-based systems to enhance natural language insight generation. Data preprocessing is a crucial step, involving data cleaning, normalization, and transformation. Both rule-based and LLM-based preprocessing methods are considered, with a detailed discussion of their advantages and disadvantages. A significant methodological contribution is the exploration of using an LLM to build a data preprocessor based on input/output dataset examples. This involves defining input/output examples, designing a preprocessing task framework, using the LLM for task identification, generating preprocessing scripts, validating and refining the code, automating the process, and enabling continuous learning. The study examines business insight extraction using both rule-based and LLM approaches, comparing their strengths and weaknesses. It also covers natural language narrative generation, comparing rule-based and LLM techniques and their advantages and disadvantages. Finally, it presents three architectures for hybrid data processing pipelines: LLM-based insight generation from chunked data, sequential data processing and insight generation, and hybrid rule-based and LLM insight generation. Each architecture is described with diagrams and a detailed explanation of its components and workflow. A benchmark is presented comparing the performance of rule-based, LLM-only, and hybrid approaches across various metrics, including precision of mathematical operations, proper name hallucination, recall of important business insights, and overall user satisfaction with reports.

The benchmarking results show that the hybrid approach consistently outperforms both purely rule-based and purely LLM-based methods across various metrics. In terms of precision of mathematical operations, the rule-based approach achieves 100% accuracy, the LLM-only approach achieves 63%, and the hybrid approach achieves 87%. For proper name hallucination, the rule-based method has 0% errors, the LLM-only approach has 12%, and the hybrid approach has 3%. In terms of recall of important business insights, the rule-based approach achieves 71%, the LLM-only approach achieves 67%, and the hybrid approach achieves 82%. Finally, the likes-to-dislikes ratio for user satisfaction on reports is 1.79 for the rule-based approach, 3.82 for the LLM-only approach, and 4.60 for the hybrid approach. These results demonstrate the effectiveness of the hybrid approach in balancing precision and accuracy with the ability to generate more comprehensive and engaging insights.

The findings strongly support the hypothesis that a hybrid LLM/rule-based approach significantly improves the generation of actionable business insights from structured data. The superior performance of the hybrid model across multiple metrics highlights the synergistic effect of combining the strengths of both rule-based and LLM methodologies. The higher precision in mathematical operations and reduced hallucination rates in the hybrid approach demonstrate its improved reliability. The increased recall of important business insights indicates its ability to capture a more comprehensive understanding of the data. The higher user satisfaction scores demonstrate that the hybrid approach generates reports that are not only accurate but also more engaging and informative for users. These results have significant relevance to the field of business intelligence, offering a practical and effective solution to the challenges of extracting and interpreting complex business data.

The study demonstrates the superior performance of hybrid LLM/rule-based systems for business insights generation. The hybrid approach effectively combines the precision of rule-based methods with the adaptability and natural language capabilities of LLMs. Future research should focus on further optimizing hybrid models, exploring different LLM architectures, and expanding the scope of business data types analyzed. This research contributes a valuable methodology for improving business intelligence and decision-making.

The study is limited by the specific LLM (GPT-4) and datasets used. The generalizability of the findings to other LLMs and datasets requires further investigation. The evaluation metrics used, while comprehensive, do not cover all aspects of business intelligence. Future research could explore additional evaluation criteria and consider a broader range of business contexts.