Text Clustering with Large Language Model Embeddings (2403.15112v5)

Abstract: 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 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.

• The paper demonstrates that OpenAI embeddings paired with k-means consistently enhance clustering accuracy across various text datasets.
• It applies diverse embedding techniques from models including BERT, Falcon, and LLaMA-2 to capture semantic context in texts.
• Findings emphasize the trade-offs between embedding size, computational efficiency, and potential information loss from summarisation.

Investigating the Impact of LLM Embeddings and Clustering Algorithms on Text Clustering

Introduction

In the field of text analysis, one of the foundational tasks is clustering, the process of grouping texts so that those within the same cluster are more similar to each other than to those in different clusters. This task is pivotal for organizing large volumes of unstructured text data into coherent categories, enhancing the efficiency of information retrieval and analysis. The advent of LLMs has introduced a new dimension to text clustering, particularly through the use of LLM-generated embeddings for text representation. This paper explores the effectiveness of different embeddings, including those derived from LLMs, across various clustering algorithms in text dataset organization.

Background

Text Embeddings

The evolution of text representation has seen significant advancements, moving from simple TF-IDF vectors to sophisticated embeddings from models like BERT and GPT. These embeddings capture semantic relationships and context, offering a richer representation of textual data. The paper explores embeddings from diverse sources including BERT, and newer LLMs like Falcon and LLaMA-2, evaluating their utility in enhancing text clustering outcomes.

Clustering Algorithms

A range of clustering algorithms including $k$-means, hierarchical clustering, and spectral clustering are employed to evaluate the efficacy of different text embeddings in clustering tasks. Each algorithm brings unique assumptions and strengths to the task of grouping texts, reflecting the diversity of approaches in text clustering.

Methodology

The methodology encompasses selecting datasets, preprocessing text, computing embeddings, applying clustering algorithms, and comparing results using a suite of metrics. Four datasets with varying characteristics were chosen to ensure a comprehensive evaluation across different text types and clustering challenges.

Results and Discussion

The results highlight the superior performance of OpenAI embeddings in clustering structured texts, outshining other LLM embeddings and traditional methods like TF-IDF. Specifically, $k$-means clustering combined with OpenAI's embeddings consistently demonstrated high performance across various metrics. Further investigation revealed that larger embeddings, such as those from Falcon-40b model, generally offered improved clustering accuracy, though at the expense of computational efficiency.

Interestingly, the paper found that summarisation techniques did not universally enhance clustering performance. This indicates that while summarisation may simplify text representations, critical information necessary for effective clustering could be lost in the process. Moreover, no single clustering algorithm dominated across all experiments, suggesting that the choice of algorithm should be attuned to the specific characteristics of the dataset and embeddings in use.

Limitations

The paper's findings are bounded by computational resources, restricting the scale of experiments, particularly those involving text summarisation and the exploration of very large embeddings. Future research could extend this work by leveraging more substantial computational power to explore these dimensions further.

Conclusions

This research contributes to the field by providing insights into how different LLM embeddings and clustering algorithms influence text clustering outcomes. The superior performance of OpenAI embeddings underscores the potential of LLMs to revolutionize text analysis tasks. However, the paper also cautions against indiscriminate reliance on summarisation or larger embeddings without considering the trade-offs in information loss and computational demands.

The findings from this paper are poised to guide future explorations in text clustering, urging a nuanced approach that balances computational efficiency with the pursuit of accuracy. Further advancements in LLM technology and clustering methodologies hold the promise of unlocking even more potent capabilities for structuring and understanding the deluge of textual data in the digital age.