Authors: Atin Sakkeer Hussain
Abstract: This paper presents the Advanced Reasoning and Transformation Engine for
Multi-Step Insight Synthesis in Data Analytics (ARTEMIS-DA), a novel framework
designed to augment Large Language Models (LLMs) for solving complex,
multi-step data analytics tasks. ARTEMIS-DA integrates three core components:
the Planner, which dissects complex user queries into structured, sequential
instructions encompassing data preprocessing, transformation, predictive
modeling, and visualization; the Coder, which dynamically generates and
executes Python code to implement these instructions; and the Grapher, which
interprets generated visualizations to derive actionable insights. By
orchestrating the collaboration between these components, ARTEMIS-DA
effectively manages sophisticated analytical workflows involving advanced
reasoning, multi-step transformations, and synthesis across diverse data
modalities. The framework achieves state-of-the-art (SOTA) performance on
benchmarks such as WikiTableQuestions and TabFact, demonstrating its ability to
tackle intricate analytical tasks with precision and adaptability. By combining
the reasoning capabilities of LLMs with automated code generation and execution
and visual analysis, ARTEMIS-DA offers a robust, scalable solution for
multi-step insight synthesis, addressing a wide range of challenges in data
analytics.
Source: http://arxiv.org/abs/2412.14146v1
