Using prompts for fine-tuning model parameters in adaptive systems like ComS2T has several potential implications:
Efficient Model Adaptation: Prompts provide a way to bridge contextual information with main observations, enabling prompt-based fine-tuning of model parameters based on distribution shifts in data.
Data Sensitivity: By training spatial-temporal prompts self-supervisedly, models can become more sensitive to changes in data distribution without requiring labeled samples for every scenario.
Generalization Capacity: Fine-tuning with prompts allows models to adapt dynamically during testing stages, enhancing their generalization capacity across various out-of-distribution scenarios.
Model Evolution: The use of prompts facilitates continuous model evolution by updating only specific parameters related to prompt representations when faced with new instances or environmental changes.
Interpretability: Prompt-based fine-tuning provides a mechanism for interpreting how external factors influence model behavior and parameter updates, enhancing transparency and interpretability of adaptive systems.