A Unified Platform for LLM-Powered Business Intelligence

View the PDF file from the paper entitled Datalab: A unified platform for the LLM business intelligence, by Luoxuan Weng and 20 other books

PDF HTML (experimental) view

a summary:Business Intelligence (BI) converts large quantities of data within modern organizations into visible visions to make enlightened decisions. Recently, the factors based on the Grand Language Model (LLM) simplified the BI workflow by automatically planning tasks, logic, and procedures in implementable environments based on natural language queries (NL). However, current methods focus mainly on individual BI tasks such as NL2SQL and NL2Vis. Dividing tasks through various roles and tools of data leads to inefficiency and potential errors due to the repetitive and cooperative nature of BI. In this paper, we offer Datalab, a uniform BI platform that integrates LLM -based frame with a reinforced mathematical front interface. Datalab supports various BI tasks for different data roles in preparing, analyzing data by combining the LLM assistance smoothly while customizing the user within one environment. To achieve this unification, we design the field of knowledge integration unit specifically designed for institutions for institutions, a communication mechanism between agents to facilitate the participation of information through the BI workflow, the cell -based context management strategy to enhance the efficiency of context use in notebooks. Wide experiences show that DATALAB is performing on the latest model on various BI tasks through common research standards. Moreover, Datalab maintains high effectiveness and efficiency on the real world data groups, which achieves an increase in accuracy by up to 58.58 % and reduces 61.65 % in the cost of the distinctive code on the BI tasks of the institution.