The Self-refine Instruction-Tuning approach can be extended to other types of reasoning tasks by adapting the methodology to suit the specific requirements of different domains. Here are some ways to extend this approach:
Domain-specific Instruction-Tuning: Tailoring the Instruction-Tuning process to the specific characteristics of the reasoning tasks in different domains. For example, for scientific reasoning tasks, the demonstrations provided by LLMs could focus on logical deductions and scientific principles.
Task Complexity: Adjusting the complexity of the reasoning tasks to match the capabilities of the SLMs. This could involve gradually increasing the difficulty of the tasks during the Self-refinement phase to ensure a smooth learning curve for the models.
Multi-modal Reasoning: Incorporating multi-modal inputs and outputs for tasks that require reasoning across different modalities, such as image and text. The demonstrations could include both visual and textual cues to train the SLMs effectively.
Transfer Learning: Leveraging transfer learning techniques to apply the Self-refine Instruction-Tuning approach to new reasoning tasks. By transferring knowledge learned from one domain to another, the models can adapt more efficiently to different types of reasoning tasks.
Evaluation Metrics: Developing specific evaluation metrics for different reasoning tasks to measure the performance and alignment between teacher and student models accurately. This ensures that the models are effectively learning the reasoning abilities required for each task.
By customizing the Self-refine Instruction-Tuning approach to suit various reasoning tasks beyond commonsense and mathematics, it can be applied effectively to a wide range of domains requiring complex reasoning abilities.