Bi Encoder

Bi-encoders are neural network architectures used primarily for efficient information retrieval, aiming to quickly find relevant information (e.g., sentences, entities, addresses) by comparing pre-computed embeddings of queries and documents. Current research focuses on improving their accuracy, particularly for unseen data and rare entities, often through techniques like contrastive learning, knowledge distillation from larger language models, and incorporating additional contextual information (e.g., entity types, geographic spans). These advancements are significant for various applications, including semantic search, named entity recognition, and question answering, offering faster and more accurate retrieval compared to computationally expensive alternatives.