Summary:

This study explores how LLM embeddings influence text clustering outcomes. The research highlights the importance of the textual embeddings, especially those stemming from Large Language Models, and their manipulation through techniques such as dimensionality reduction and summarization to achieve more accurate clustering. Key outcomes are:

• Analysis of LLM embeddings in capturing structured language nuances.
• Assessment of BERT and its performance in lightweight embedding scenarios.
• Discussion on the impact of embedding size and summarization techniques.

This research undercs…