What process improves a machine learning model's performance on specific tasks by following explicit instructions?

Instruction tuning is a process that enhances a machine learning model's ability to perform specific tasks by fine-tuning it according to explicit instructions or guidelines. This technique allows the model to better understand and execute tasks by aligning it more closely with the intended objectives and expected outputs.

In practice, instruction tuning involves providing the model with examples that demonstrate how to interpret and respond to particular directives. This can significantly improve the model's effectiveness, especially in applications where precise task execution is crucial. By training the model on various instructions and how to respond appropriately, it learns to generalize from these instructions to handle new, similar tasks competently.

The improvement in performance comes from the model being made aware of the specific requirements and constraints of the tasks it is expected to perform, making it more adept in those scenarios. This contrasts with other processes, such as reinforcement learning, which optimizes performance based on feedback from the environment, or transfer learning, where knowledge gained from one domain is applied to a different but related domain. Parameter tuning focuses on adjusting the model's hyperparameters to achieve better performance, rather than specifically guiding the model through explicit instructions.