J arvi X : A LLM No code Platform for Tabular Data Analysis and Optimization (2023)

Abstract: In this study, we introduce JarviX, a sophisticated data analytics framework. JarviX is designed to employ Large Language Models (LLMs) to facilitate an automated guide and execute high-precision data analyzes on tabular datasets. This framework emphasizes the significance of varying column types, capitalizing on state-of-the-art LLMs to generate concise data insight summaries, propose relevant analysis inquiries, visualize data effectively, and provide comprehensive explanations for results drawn from an extensive data analysis pipeline. Moreover, JarviX incorporates an automated machine learning (AutoML) pipeline for predictive modeling. This integration forms a comprehensive and automated optimization cycle, which proves particularly advantageous for optimizing machine configuration. The efficacy and adaptability of JarviX are substantiated through a series of practical use case studies.

## introduction although the predominant focus of contemporary research on large language models is the evaluation of various tasks @xcite , there is a noticeable lack of academic resources that provide structured guidelines and frameworks for downstream applications. tabular data analysis, as an important application task of llms, has always faced challenges related to the precision of mathematical calculations. despite its ability to address complex high school math problems and participate in advanced mathematical discussions, advanced models such as are not yet on par with expert level performance @xcite . they are prone to basic errors and can sometimes produce incoherent outputs. llm guides users through a rule-based system in jarvix, which enables data visualization and statistical analysis. it leverages a vectorized domain knowledge repository to provide relevant explanations for each visualization and suggests further exploration directions @xcite . users can generate additional exploratory charts and insights through text or voice input, facilitated by whisper. 1 these insights are processed by the vicuña model foot_1 through prompts fine-tuned by gpt-4 foot_2 . this approach culminates in a comprehensive report encapsulating all the insights and analytical processes, serving as a thorough guide for users and a blueprint for future analysis. furthermore, this study explores the integration of h2o-automl-customized automl pipelines (h2o.ai, 2023) into this process. the use of au-toml is demonstrated to identify the best targets with respect to other data columns and to build specific models to optimize results for various objectives, such as optimal factory configurations @xcite . the primary objective of this study is to empower users with the knowledge and tools necessary to harness the power of llm for rule-based data analytics by fine-tuning @xcite and automl. the paper concludes by underlining the potential of this approach in democratizing data analytics, thereby fostering more strategic and informed decision-making. ## overview jarvix is a no-code platform for efficient analysis and optimization of tabular data, handling both structured and unstructured types, as illustrated in figure 1 . for structured data(e.g., csv files, data frames), it performs preliminary processing tasks including data type detection, statistical computation, and correlation analysis, storing the results in a postgres database. unstructured data (e.g., text files, audio files) is managed through text extraction and embedding, followed by storage in a vectorized database, such as elastic search. users can interact with the platform through three key features: jarvix insight, natural language interfaces, and jarvix guidance. jarvix insight collects structured data information such as column names, types, and statistical data, and employs a llm to generate a data summary report, providing users with an understanding of their data and identifying key questions. the natural language interfaces feature accommodates user queries about their datasets, either voice-to-text or typed, and translates these queries into a rule-based system via a fine-tuned llm. this delivers relevant data visualizations, explanations, and follow-up question suggestions. lastly, jarvix guidance assists users through a step-by-step data analysis process. it takes into account the user's understanding of the datasets, their role, the specific dataset, and target column they wish to analyze. using this information, jarvix anticipates the questions a user might want to address first and commences the result generation process. it also provides an appropriate endpoint for analysis for each user. all stages of the analysis are recorded, including the middle result i , and compiled into a comprehensive report that users can save and share. jarvix integrates a data cleaning interface with automated functions, enabling users to efficiently perform data cleaning. then, the system initiates data pre-processing, which includes automatic type detection, column statistics computation, and calculation of the correlation matrix between columns. it is worth noting that these tasks-column statistical computation and correlation matrix calculation-are executed asynchronously, ensuring that user progress isn't hindered. in handling unstructured data, we leverage various connectors in the llama hub @xcite to perform text extraction. the extracted data is stored in the vector database using faiss (meta, 2023) and assigned the same project id as the structured database. this cohesive data management strategy ensures seamless integration and retrieval of both structured and unstructured data. ## case study in this section, we demonstrate two separate use cases: 1) utilizing jarvix insight for custom analysis with data matching, and 2) exploring jarvix's guidance use cases. ## conclusion jarvix, by integrating llm and automl technologies, presents a unique and all-encompassing approach for the analysis of tabular data. the system integrates non-structural data to generate profound insights, employing llm to aid users in their data exploration endeavors.