Knowledge Grounding

Knowledge grounding aims to enhance language models' ability to utilize external knowledge sources, such as structured databases and knowledge graphs, for improved response generation and question answering. Current research focuses on developing methods to effectively integrate this external knowledge with large language models (LLMs), often employing techniques like instruction tuning, contextual latent interaction, and knowledge graph embeddings within various architectures including transformer-based models. This work is significant because it addresses limitations of LLMs in handling complex queries, reducing hallucinations, and improving the accuracy and relevance of responses in applications ranging from conversational agents to complex question answering systems.