by further fine-tuning on individual downstream tasks.

3. They evaluate the models on a variety of tasks encompassing natural language understanding, reasoning, and question answering.

The implications of the study are:

1. The combination of MoE models and instruction tuning can lead to more efficient and effective large-scale language models.

2. The study suggests a reevaluation of the design principles of large-scale, high-performance language models in the framework of task-agnostic learning.

3. The