Retrieval Method

Retrieval methods enhance large language models (LLMs) by dynamically incorporating external information, addressing LLMs' limitations in handling up-to-date knowledge and improving accuracy. Current research focuses on improving retrieval efficiency and effectiveness, exploring various architectures like bi-encoders and novel techniques such as multi-hop retrieval and chunking-free in-context retrieval to better integrate retrieved information with LLMs. This work is significant because it enables LLMs to access and utilize a much broader knowledge base, leading to more accurate and reliable outputs across diverse applications, including question answering, text generation, and text classification.