PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning (2505.09519v1)

Abstract: Parameter-efficient fine-tuning (PEFT) methods have shown promise in adapting LLMs, yet existing approaches exhibit counter-intuitive phenomena: integrating router into prompt tuning (PT) increases training efficiency yet does not improve performance universally; parameter reduction through matrix decomposition can improve performance in specific domains. Motivated by these observations and the modular nature of PT, we propose PT-MoE, a novel framework that integrates matrix decomposition with mixture-of-experts (MoE) routing for efficient PT. Results across 17 datasets demonstrate that PT-MoE achieves state-of-the-art performance in both question answering (QA) and mathematical problem solving tasks, improving F1 score by 1.49 points over PT and 2.13 points over LoRA in QA tasks, while enhancing mathematical accuracy by 10.75 points over PT and 0.44 points over LoRA, all while using 25% fewer parameters than LoRA. Our analysis reveals that while PT methods generally excel in QA tasks and LoRA-based methods in math datasets, the integration of matrix decomposition and MoE in PT-MoE yields complementary benefits: decomposition enables efficient parameter sharing across experts while MoE provides dynamic adaptation, collectively enabling PT-MoE to demonstrate cross-task consistency and generalization abilities. These findings, along with ablation studies on routing mechanisms and architectural components, provide insights for future PEFT methods.

PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning

The presented paper introduces PT-MoE, an advanced framework designed to enhance parameter-efficient fine-tuning (PEFT) methodologies within LLMs. The authors aim to address the inefficiencies observed when integrating mixture-of-experts (MoE) architectures with prompt tuning (PT). Unlike traditional approaches, PT-MoE strategically merges matrix decomposition techniques with MoE routing to optimize performance across diverse tasks while minimizing parameter utilization.

Key Findings and Contributions

The PT-MoE framework stands out for its ability to concurrently enhance model efficiency and performance. Evaluations conducted across 17 distinct datasets, encompassing both question-answering (QA) and mathematical problem-solving tasks, reveal that PT-MoE not only achieves state-of-the-art results but also significantly reduces parameter requirements. Specifically, PT-MoE improves the F1 score by 1.49% over traditional PT and by 2.13% over LoRA in QA tasks. In mathematical accuracy, it surpasses PT by 10.75 points and LoRA by 0.44 points, all while employing 25% fewer parameters than LoRA.

Three notable contributions of PT-MoE include:

1. Innovative Architecture: The integration of low-rank matrix decomposition with MoE routing creates a novel framework that leverages dynamic expert selection and efficient parameter sharing, facilitating improved generalization across tasks.
2. Comprehensive Analysis: Extensive empirical evaluations and ablation studies highlight the impact of various architectural components, such as prompt length, expert count, and routing mechanisms, on the performance of PT-MoE.
3. Guidelines for Future PEFT Approaches: Insights derived from the analysis inform future developments in PEFT methods, with particular emphasis on optimizing both performance and parameter efficiency.

Practical and Theoretical Implications

The findings demonstrate PT-MoE's potential to significantly reduce computational and resource costs associated with fine-tuning large-scale models. This has profound implications, particularly in low-resource settings where traditional full model fine-tuning is impractical. Moreover, the complementary benefits of matrix decomposition and MoE routing observed in PT-MoE suggest new avenues for research in scalable model adaptation techniques.

Theory-wise, PT-MoE contributes to the understanding of parameter efficiency and model performance interplay within LLMs. The approach also underscores the nuanced dynamics of prompt optimization, challenging existing assumptions about parameter-sharing strategies in PEFT.

Future Directions

PT-MoE opens several paths for future exploration. One area of interest is the extension of the framework to continual learning scenarios, thereby enhancing the model's adaptability across evolving tasks. Additionally, refining routing mechanisms, potentially through hierarchical or probabilistic models, could further improve task distribution and expert selection, optimizing cross-domain performance.

In summary, PT-MoE represents a significant advancement in PEFT, offering a sophisticated, efficient approach to model adaptation. Its ability to balance high performance with reduced parameter usage is crucial for advancing the deployment of LLMs in diverse and computationally constrained environments.