Unseen Entity

Research on "unseen entities" focuses on developing machine learning models capable of handling data points or concepts not encountered during training. Current efforts concentrate on improving generalization through techniques like instruction tuning, knowledge graph integration, and multi-modal learning, often employing transformer-based architectures and meta-learning approaches. This work is crucial for advancing various fields, including natural language processing, computer vision, and automated reasoning, by enabling more robust and adaptable AI systems in real-world applications where novel data is inevitable. The ultimate goal is to create systems that can effectively learn and reason about new information without requiring extensive retraining.