ESMC: MLLM-Driven Embedding Selection for Explainable Clustering
Analysis
This ArXiv paper explores the use of Multilingual Large Language Models (MLLMs) for improving the explainability of multiple clustering tasks. The approach, ESMC, focuses on selecting embeddings to enhance understanding of cluster formation.
Key Takeaways
- •ESMC utilizes MLLMs for selecting optimal embeddings.
- •The research aims to improve explainability in multiple clustering.
- •The paper is based on work submitted to ArXiv, indicating early-stage research.
Reference / Citation
View Original"ESMC leverages MLLMs for embedding selection."