Topic Modeling for Heterogeneous Digital Libraries: Tailored Approaches Using Large Language Models

Digital libraries hold vast and diverse content, with electronic theses and dissertations (ETDs) being among the most diverse. ETDs span multiple disciplines and include unique terminology, making achieving clear and coherent topic representations challenging. Existing topic modeling techniques often struggle with such heterogeneous collections, leaving a gap in providing interpretable and meaningful topic labels. This thesis addresses these challenges through a three-step framework designed to improve topic modeling outcomes for ETD metadata. First, we developed a custom preprocessing pipeline to enhance data quality and ensure consistency in text analysis. Second, we applied and optimized multiple topic modeling techniques to uncover latent themes, including LDA, ProdLDA, NeuralLDA, Contextualized Topic Models, and BERTopic. Finally, we integrated Large Language Models (LLMs), such as GPT-4, using prompt engineering to augment traditional topic models, refining and interpreting their outputs without replacing them. The framework was tested on a large corpus of ETD metadata, including through preliminary testing on a small subset. Quantitative metrics and user studies were used to evaluate performance, focusing on the clarity, accuracy, and relevance of the generated topics. The results demonstrated significant improvements in topic coherence and interpretability, with user study participants highlighting the value of the enhanced representations. These findings underscore the potential of combining customized preprocessing, advanced topic modeling, and LLM-driven refinements to better represent themes in complex collections like ETDs, providing a foundation for downstream tasks such as searching, browsing, and recommendation.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Dasu, Pradyumna Upendra

Chair dc:contributor.committeechair

• Fox, Edward A.

Committee members dc:contributor.committeemember

• Wang, Xuan
• Chen, Yinlin

• Topic Modeling
• Natural Language Processing
• Large Language Models
• Electronic Theses and Dissertations
• Digital Libraries
• Information Storage and Retrieval
• Artificial Intelligence
• Search and Recommendation

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:42363

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/124154