Are Large Language Models the New Interface for Data Pipelines?

TL;DR

This paper discusses the potential of Large Language Models (LLMs) as a new interface for data pipelines, highlighting their capabilities in understanding, generating, and extracting insights from data to enhance AI applications.

Contribution

It explores how LLMs can be integrated into data pipelines to improve data processing, analysis, and decision-making across various AI-related fields.

Findings

LLMs enable innovative applications in XAI, AutoML, and Knowledge Graphs.

They can extract valuable insights for Big Data Analytics.

Potential to improve data pipeline efficiency and intelligence.

Abstract

A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like fluency and coherence, making them valuable for a wide range of data-related tasks fashioned as pipelines. The capabilities of LLMs in natural language understanding and generation, combined with their scalability, versatility, and state-of-the-art performance, enable innovative applications across various AI-related fields, including eXplainable Artificial Intelligence (XAI), Automated Machine Learning (AutoML), and Knowledge Graphs (KG). Furthermore, we believe these models can extract valuable insights and make data-driven decisions at scale, a practice commonly referred to as Big Data Analytics (BDA). In this position paper, we provide some discussions in the direction of unlocking synergies among these technologies, which can lead to more powerful and intelligent AI solutions, driving improvements in data pipelines across a wide range of applications and domains integrating humans, computers, and knowledge.