To extend the proposed methods for handling dynamic changes in the instruction set, particularly with the addition of new instruction categories, several strategies can be implemented. First, a continuous monitoring system can be established to regularly assess the performance of the language model (LLM) as new instructions are introduced. This system would utilize the existing correlation and dependency patterns to evaluate how the new categories interact with the current instruction set.
Second, the automatic tagging system can be adapted to incorporate new instructions by generating tags that describe the capabilities and knowledge required for these instructions. This would involve retraining the tagging model periodically to ensure it remains up-to-date with the latest instruction categories.
Third, the effect equivalence-based optimization method can be modified to include new categories by recalculating the effect equivalence coefficients. This recalibration would allow the optimization process to account for the interactions between the new and existing categories, ensuring that the instruction set remains balanced and effective.
Lastly, a feedback loop can be established where the performance data from the LLM is used to refine the dependency taxonomy and correlation patterns continuously. This iterative approach would enable the model to adapt to changes in the instruction set dynamically, ensuring that the learning schema remains effective as new categories are added.