Entity Knowledge

Entity knowledge in large language models (LLMs) focuses on understanding how these models represent and utilize information about real-world entities, aiming to improve accuracy, robustness, and control over their knowledge base. Current research emphasizes methods for injecting, evaluating, and even unlearning entity knowledge, employing techniques like instruction tuning, graph neural networks, and contrastive learning within various model architectures including transformers and encoder-decoder frameworks. This research is crucial for addressing concerns about misinformation, bias, and privacy in LLMs, while also improving performance on downstream tasks such as named entity recognition, question answering, and knowledge graph completion.