- Unveiling the Realm of AI Technologies: A Glimpse into the Augmented Future
- Generative AI and Language Models
- LLMs and Their Applications
- LLMs and Customer Support
- Development, Optimization, Localization, and Personalization Based on LLMs
- Unveiling the Power of Clustering and Topic Modeling
- Enhancing Customer Support Through Hybrid AI: LLMs Meet Clustering and Topic Modeling
- Endnotes
Unveiling the Power of Clustering and Topic Modeling
Despite the rapid evolution of LLMs that can produce coherent and diverse texts across various domains, many tasks still require more granular and structured analysis of textual data. Clustering and topic modeling are techniques that can help discover hidden patterns, themes, and categories in a large collection of documents, without relying on predefined labels or annotations. They can also help reduce the data’s dimensionality and complexity, making it easier to visualize, summarize, and interpret.
There are some example applications where clustering and topic modeling can be useful, such as:
Document classification and retrieval: Clustering and topic modeling can help search and navigate large collections of documents by grouping similar ones according to their content. Moreover, they can also facilitate the identification of relevant documents for a given query or task.
Text summarization and generation: Although LLMs can also be utilized for text summarization and clustering, topic modeling can supplement LLMs by extracting the main topics and keywords from the targeted collections of documents and providing concise and informative summaries that capture the essence and different granularities of the data. They can also serve as input or an additional layer for text generation systems, such as LLMs, that can produce longer and more detailed texts based on the topics and keywords.
Sentiment analysis and opinion mining: Although LLMs have shown remarkable performance in understanding the context and capturing nuances in natural languages, topic modeling, and clustering methods, taking Latent Dirichlet Allocation (LDA) or K-mean clustering as examples can be more interpretable and can provide insights into the main themes in a collection of texts.23 Utilizing a hybrid approach that combines both might be a good solution. For instance, using LLMs for fine-grained sentiment analysis and using topic modeling to understand broader themes or trends.
Knowledge discovery and extraction: By uncovering the latent concepts and relations among the documents, clustering and topic modeling can enrich the semantic representation of the data, as well as the knowledge base of the domain. They can also help to identify gaps and inconsistencies in the data, as well as new and emerging topics and issues.
Therefore, clustering and topic modeling are still necessary and valuable tools for many tasks that involve understanding, analyzing, and generating textual data, especially when the data is large, heterogeneous, and unlabeled. They can complement and enhance the capabilities of LLMs’ capabilities and provide insights and feedback for improving their performance and quality.