Knowledge Intensive

Knowledge-intensive tasks in natural language processing (NLP) focus on enabling AI systems to effectively utilize and reason with large amounts of external knowledge to answer complex questions and perform sophisticated reasoning. Current research emphasizes improving the efficiency and accuracy of integrating knowledge into language models, exploring techniques like retrieval-augmented generation, knowledge graph integration, and various prompt engineering strategies to enhance both model performance and inference speed. These advancements are crucial for building more robust and reliable NLP systems with applications ranging from question answering and fact verification to scientific knowledge discovery and cross-lingual information retrieval.