While the dependency-aware training sample construction and list-pairwise training approach in the DemoRank framework offer significant advantages, they also present certain limitations:
Computational Complexity: The iterative process of constructing dependency-aware training samples can be computationally intensive, especially as the number of demonstrations increases. This may lead to longer training times and higher resource consumption.
Improvement: To mitigate this, techniques such as pruning the demonstration pool based on initial relevance scores could be employed to reduce the number of candidates considered in each iteration. Additionally, parallel processing could be utilized to speed up the scoring of demonstration lists.
Quality of Demonstrations: The effectiveness of the dependency-aware training samples heavily relies on the quality of the retrieved demonstrations. If the initial retrieval does not yield high-quality demonstrations, the subsequent training will be adversely affected.
Improvement: Implementing a feedback loop where the performance of the selected demonstrations is evaluated and used to refine the retrieval process could enhance the quality of the demonstrations. This could involve using reinforcement learning techniques to optimize the selection process based on performance metrics.
Limited Generalization: The list-pairwise training approach may struggle to generalize well to unseen data or tasks that differ significantly from the training set, as it relies on the specific dependencies learned during training.
Improvement: To enhance generalization, incorporating diverse datasets during training and employing domain adaptation techniques could help the model learn more robust representations. Additionally, using ensemble methods that combine multiple models trained on different datasets could improve performance on unseen tasks.
By addressing these limitations, the DemoRank framework can be further refined to enhance its robustness and applicability across various scenarios.