Knowledge Retrieval

Knowledge retrieval aims to efficiently access and integrate relevant information from diverse sources to enhance the performance and interpretability of large language models (LLMs). Current research focuses on improving retrieval accuracy and efficiency through techniques like retrieval-augmented generation (RAG), prompt engineering (including Chain-of-Thought and self-consistency methods), and the development of novel architectures that leverage LLMs' internal states for adaptive retrieval. These advancements are significant for improving the reliability and factual accuracy of LLMs across various applications, from question answering and medical diagnosis to design and education.