Large Language Models for Data Discovery and Integration

This technical report examines the challenges and opportunities presented by Large Language Models (LLMs) in the fields of data discovery and integration. The exponential growth of data from various sources necessitates effective methods for discovering and integrating datasets. Traditional approaches face limitations, particularly regarding semantic heterogeneity and ambiguity, which hinder their effectiveness. LLMs have emerged as a promising solution, capable of contextualizing datasets and demonstrating generalization capabilities without task-specific fine-tuning. This paper synthesizes recent developments in LLM applications for data tasks, identifies limitations in current methods, and suggests potential directions for future research. It discusses the processes involved in dataset discovery and integration, the challenges posed by semantic inconsistencies, and how LLMs can address these issues. The report also highlights the need for further exploration of LLM-driven solutions to improve data management practices across various domains.