Different Word Embeddings

Different word embeddings, numerical representations of words, are crucial for various natural language processing tasks. Current research focuses on comparing the performance of various embedding types, including those derived from large language models (LLMs) like BERT, and traditional methods such as TF-IDF and Word2Vec, across different applications such as text clustering, image generation, and covariate drift detection. These studies often investigate the interplay between embedding choice, dimensionality reduction techniques, and the specific downstream task, aiming to optimize performance and robustness. The findings inform the development of more effective and efficient natural language processing systems across diverse fields.