Task Vector

Task vectors represent the learned weight differences between a pre-trained model and its fine-tuned version for a specific task, effectively encoding task-specific knowledge. Current research focuses on leveraging task vectors for various applications, including improving in-context learning, mitigating biases, enabling efficient multi-task learning through arithmetic operations on these vectors, and enhancing model compression and merging. This approach offers a powerful method for manipulating and understanding the knowledge embedded within large language models and other neural networks, with implications for improving model efficiency, robustness, and ethical considerations. The ability to manipulate and combine task vectors offers a promising avenue for creating more adaptable, efficient, and ethically sound AI systems.