Text Embeddings

Text embeddings are numerical representations of text that capture semantic meaning, enabling computers to understand and process language. Current research focuses on improving the quality and controllability of these embeddings, particularly through techniques like contrastive learning, fine-tuning large language models (LLMs), and developing novel architectures to better handle complex prompts and disentangle attributes within embeddings. These advancements are crucial for various applications, including image generation, information retrieval, and sentiment analysis, improving the performance and efficiency of numerous natural language processing tasks.