Text Clustering with Large Language Model Embeddings

01/12/2025

Text clustering is an important method for organising the increasing volume of digital content, aiding in the structuring and discovery of hidden patterns in uncategorised data. The effectiveness of text clustering largely depends on the selection of textual embeddings and clustering algorithms. This study argues that recent advancements in large language models (LLMs) have the potential to enhance this task. The research investigates how different textual embeddings, particularly those utilised in LLMs, and various clustering algorithms influence the clustering of text datasets. A series of experiments were conducted to evaluate the impact of embeddings on clustering results, the role of dimensionality reduction through summarisation, and the adjustment of model size. The findings indicate that LLM embeddings are superior at capturing subtleties in structured language. OpenAI’s GPT-3.5 Turbo model yields better results in three out of five clustering metrics across most tested datasets. Most LLM embeddings show improvements in cluster purity and provide a more informative silhouette score, reflecting a refined structural understanding of text data compared to traditional methods. Among the more lightweight models, BERT demonstrates leading performance. Additionally, it was observed that increasing model dimensionality and employing summarisation techniques do not consistently enhance clustering efficiency, suggesting that these strategies require careful consideration for practical application. These results highlight a complex balance between the need for refined text representation and computational feasibility in text clustering applications. This study extends traditional text clustering frameworks by integrating embeddings from LLMs, offering improved methodologies and suggesting new avenues for future research in various types of textual analysis.

Autores da comunidade :

• 

  N

  Nuno Fachada

  ist145239

  Afiliações e fontes

  IST > LARSYS

  Laboratório de Robótica e Sistemas em Engenharia e Ciência

VoR - Versão publicada

Elsevier

https://www.sciencedirect.com/science/article/pii/S2666307424000482

International Journal of Cognitive Computing in Engineering

100

108

6

2666-3074

10.1016/j.ijcce.2024.11.004

computer-and-information-sciences - Ciências da Computação e da Informação

Palavras-chave

• Text clustering
• Large language models
• LLMs
• Text summarisation

eng - Inglês

CC-BY - CC-BY