From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models

The review titled 'From Word Vectors to Multimodal Embeddings' provides a comprehensive overview of the advancements in word embeddings and language models, which have revolutionized natural language processing (NLP). It details the transition from traditional sparse representations to dense embeddings like Word2Vec, GloVe, and fastText, and discusses the evolution of models such as ELMo, BERT, and GPT. These models have not only enhanced NLP capabilities but have also found applications in multimodal fields, including vision and robotics. The review emphasizes the importance of addressing technical challenges and ethical implications associated with these technologies. Furthermore, it outlines future research directions, highlighting the necessity for scalable training techniques and improved interpretability, which are crucial for the responsible development of AI systems.