Abstract

The digital landscape increasingly generates vast amounts of unstructured textual data,

valuable for analytics and various machine learning (ML) applications. These vast stores of

data, often likened to digital gold, are often challenging to process and utilize. Traditional text

processing methods, lacking the ability to generalize, typically struggle with unstructured and

unlabeled data. For many complex data management workflows, the solution typically involves

human intervention in the form of manual curation and labeling — a time-consuming process.

Large Language Models (LLMs) are AI models trained on vast amounts of text data. They have

remarkable Natural Language Processing (NLP) capabilities and offer a promising alternative.

This thesis serves as an empirical case study of LLMs as advanced data preprocessing tools. It

explores the effectiveness and limitations of using LLMs to automate and refine traditionally

challenging data preprocessing tasks, highlighting a critical area of research in data

management. An LLM-based preprocessing pipeline, designed to clean and prepare raw textual

data for use in ML applications, is implemented and evaluated. This pipeline was applied to a

corpus of unstructured text documents, extracted from PDFs, with the aim of transforming them

into a fine-tuning dataset for LLMs. The efficacy of the LLM-based preprocessing pipeline was

assessed by comparing the results against a manually curated benchmark dataset using two

text similarity metrics: the Levenshtein distance and ROUGE score. The findings indicate that

although LLMs are not yet capable of fully replacing human curation in complex data

management workflows, they substantially improve the efficiency and manageability of

preprocessing unstructured textual data.