Enhancing Multi-Task Learning Accuracy with Learnable and Interpretable Modules
In this article, we delve into the potential of integrating learnable and interpretable modules, specifically Kolmogorov-Arnold Networks (KAN) and graph-based representations, within a pre-trained GPT-2 model to enhance multi-task learning accuracy.
This research is motivated by the recent surge in utilizing KAN and graph attention architectures like Graph LoRA and Hybrid-KAN LoRA (Learnable GPT) in chain-of-thought (CoT) models. These models have sparked debates over their benefits compared to simpler architectures like Multi-Layer Perceptrons (MLPs).
The initial approach involves enhancing a standard self-attention transformer using Low-Rank Adaptation (LoRA) along with fine-tuning hyperparameters and incorporating L2 regularization. Notably, these enhancements lead to significant improvements in performance.
However, for greater interpretability and richer representations, the researchers also developed two variants: Graph LoRA and Hybrid-KAN LoRA. The Graph LoRA model aims to improve the standard KAN and the Hybrid-KAN LoRA model combines the benefits of KAN and GAT architectures.
Despite these efforts, systematic evaluations indicate that neither variant outperforms the optimized LoRA-enhanced transformer. The optimized transformer achieved an accuracy of 55.249% on the SST test set, 99.18% on the CFIMDB dev set, and 89.9% paraphrase detection test accuracy. When it comes to sonnet generation, the optimized transformer achieved a CHRF score of 42.097.
These findings highlight the importance of efficient parameter adaptation through LoRA as the most effective strategy for the tasks of sentiment analysis, paraphrase detection, and sonnet generation. The LoRA-enhanced transformer demonstrates superior performance compared to the variants with learnable and interpretable modules.
This study provides valuable insights into the potential trade-offs between complexity and performance in model architectures. While KAN and graph attention architectures have gained popularity due to their interpretability, this research shows that simpler models with optimized adaptations can deliver better results in certain contexts.
Future Directions
Further exploration is essential to gain a deeper understanding of the limitations of current learnable and interpretable modules. While the LoRA-enhanced transformer has proven effective in the tasks at hand, there may be other scenarios where different module combinations could yield superior results.
It would also be interesting to investigate the impact of different hyperparameter settings and regularization techniques on the performance of the learnable and interpretable modules. This could potentially uncover new avenues for improving these architectures.
Additionally, extending the evaluation to different datasets and tasks would provide a more comprehensive analysis of the generalizability of the findings. Each task has its own challenges and requirements, and exploring a wider range of applications could shed light on the strengths and weaknesses of these module combinations.
In conclusion, while the LoRA-enhanced transformer proves to be the most effective strategy for sentiment analysis, paraphrase detection, and sonnet generation, there are still opportunities for further research to refine and expand upon these results. The integration of learnable and interpretable modules remains a fascinating area of exploration in the quest for enhanced multi-task learning accuracy.