Which of the following techniques is considered an advanced method in optimizing large language models?

INT8 Quantization with Calibration is recognized as an advanced method for optimizing large language models because it significantly reduces the memory footprint and computational requirements of these models while maintaining a high level of accuracy. This technique involves converting the model's weights and activations from floating-point representation (typically float32) to a lower precision, 8-bit integer format. This conversion allows for more efficient storage, faster computation, and reduced energy consumption, which is critical when deploying large models in resource-constrained environments such as mobile devices or edge servers.

The process of calibration ensures that the quantized model retains performance by adjusting the scaling factors used in quantization, optimizing the balance between accuracy and efficiency. This means that even after reducing the precision, the model is still able to perform effectively on its tasks, making INT8 quantization a powerful method for optimizing large language models.

Other methods like model compression involve reducing the size of the model without necessarily focusing on quantization techniques, while transfer learning typically reuses knowledge from pre-trained models rather than directly optimizing them for deployment. Structural optimization, meanwhile, alters the architecture of the model, which is indeed an advanced method, but it does not specifically focus on the computational efficiency and resource utilization that quantization addresses directly.