Revisiting Data Analysis with Pre-trained Foundation Models

TL;DR

The paper surveys how Pre-trained Foundation Models (PFMs) can systematically enhance data analysis by enabling scalable reasoning, improved accessibility, better data quality, and automated workflows. It argues that PFMs support symbolic concept handling, PAC-style in-context learning, and cross-modal generalization, while also offering representation learning, DSL consolidation, and interpretable/editable reasoning to improve reliability. The work outlines a comprehensive framework of PFMs-enhanced methodologies across reasoning, interface design, data quality, and AutoML, and discusses critical challenges such as inference costs, domain generalization, consistency, theory integration, and trust. It concludes with a roadmap for integrating PFMs into data analysis in a principled manner, balancing innovation with foundational methods to maximize practical impact. Overall, PFMs are positioned as a versatility-driven driver for scalable, interpretable, and automated data analysis, provided that cost, reliability, and theoretical alignment are carefully managed.

Abstract

Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume and variety of research emerged, addressing datasets of diverse modalities, formats, scales, and resolutions across various industries. However, experienced data analysts often find themselves overwhelmed by intricate details in ad-hoc solutions or attempts to extract the semantics of grounded data properly. This makes it difficult to maintain and scale to more complex systems. Pre-trained foundation models (PFMs), grounded with a large amount of grounded data that previous data analysis methods can not fully understand, leverage complete statistics that combine reasoning of an admissible subset of results and statistical approximations by surprising engineering effects, to automate and enhance the analysis process. It pushes us to revisit data analysis to make better sense of data with PFMs. This paper provides a comprehensive review of systematic approaches to optimizing data analysis through the power of PFMs, while critically identifying the limitations of PFMs, to establish a roadmap for their future application in data analysis.

Figures (7)

• Figure 1: A framework for data analysis tasks and challenges. (a) Data preparation: handling data preparation and ensuring quality for analysis. Exploration: facilitating interactive analysis to uncover trends and patterns. Implementation: applying specific methods for reasoning, modeling, and decision-making. Assessment: validating results and ensuring reliability. These tasks interact with and overlap with each other. (b) PFMs address the challenges of accessibility, quality optimization, and automation, powered and regularized by PMF-enhanced reasoning, which in turn enable the effective execution of these data analysis tasks. These applications and challenges are detailed in § \ref{['sec:methods']}. PFMs commute specific tasks with the essence of intelligent ability provided by previous tools and theories.
• Figure 2: An Overview of How PFMs Empower Data Analysis Tasks Through Optimizations. The figure illustrates the relationships between key data analysis tasks (left column)—Data Management, Exploratory Data Analysis, Implementation, and Assessment—and the optimizations enabled by Pre-trained Foundation Models (PFMs) (right column). The middle column lists representative methods and studies that bridge specific tasks to the corresponding optimizations. Arrows indicate how PFMs address challenges within each task, facilitating the transition towards optimized data analysis processes. This visual framework underscores the multifaceted role of PFMs in systematically enhancing data analysis by connecting practical tasks with advanced optimization strategies and the core role of scaling and consolidating domain-specific language (DSL) during data analysis.
• Figure 3: Representation learning of concepts and samples. (a) Implementation of concepts in one computational model can be represented by more concise symbolic forms. (e.g. func-names in compiled libraries.) (b) Structures of interest can be extracted and aligned as inference and modeling processes. They are identified by Representation learning is recompiling and compressing algorithms and datasets.
• Figure 4: Two kinds of reasoning. (a) Deduction provides top-down reasoning that proves sufficiency between statements and conclusions, which generates special cases/samples according to universal principles and rules or hypotheses. (b) Induction provides bottom-up reasoning that proves the necessity between samples and conclusions, which approximate and conclude principles and rules from cases/samples. Other kinds of reasoning can be seen as compositions of these two kinds of reasoning while introducing inconsistencies and approximations to compromise consistency and completeness.
• Figure 5: PFM-based reasoning algorithm. (a) True statements can be produced by inconsistent reasoning due to high validity. E.g., $q$ is necessarily satisfied according to $p, p\rightarrow q$, which provides formal validity from classical logic. (b) Adjusting the expressiveness by approximation compromises the decidability and completeness of the reasoning algorithm. Essential factors for PFMs' augmented reasoning lie in these mechanisms.