Neural Retriever

Neural retrievers are advanced information retrieval systems using deep learning to find relevant information within large datasets, aiming to surpass traditional methods in accuracy and efficiency. Current research focuses on improving retrieval speed, addressing biases towards specific data sources (like LLM-generated text), enhancing multilingual capabilities (including zero-shot transfer learning), and developing more data-efficient training methods such as self-training and prompt tuning. These improvements have significant implications for various applications, including question answering, search engines, and knowledge base access, particularly in low-resource language settings.