Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts
arXiv:2603.03535v1 Announce Type: new Abstract: While large language models (LLMs) fine-tuned with lightweight adapters achieve strong performance across diverse tasks, their performance on individual tasks depends on the fine-tuning strategy. Fusing independently trained models with different strengths has shown promise for multi-task learning through three main strategies: ensembling, which combines outputs from independent models; merging, which fuses model weights via parameter averaging; and routing, which integrates models in an input-dependent fashion. However, many design decisions in these approaches remain understudied, and the relative benefits of more sophisticated ensembling, merging and routing techniques are not fully understood. We empirically evaluate their trade-offs, addressing two key questions: What are the advantages of going beyond uniform ensembling or merging? And does the flexibility of routing justify its complexity? Our findings indicate that non-uniform en
arXiv:2603.03535v1 Announce Type: new Abstract: While large language models (LLMs) fine-tuned with lightweight adapters achieve strong performance across diverse tasks, their performance on individual tasks depends on the fine-tuning strategy. Fusing independently trained models with different strengths has shown promise for multi-task learning through three main strategies: ensembling, which combines outputs from independent models; merging, which fuses model weights via parameter averaging; and routing, which integrates models in an input-dependent fashion. However, many design decisions in these approaches remain understudied, and the relative benefits of more sophisticated ensembling, merging and routing techniques are not fully understood. We empirically evaluate their trade-offs, addressing two key questions: What are the advantages of going beyond uniform ensembling or merging? And does the flexibility of routing justify its complexity? Our findings indicate that non-uniform ensembling and merging improve performance, but routing offers even greater gains. To mitigate the computational cost of routing, we analyze expert selection techniques, showing that clustering and greedy subset selection can maintain reasonable performance with minimal overhead. These insights advance our understanding of model fusion for multi-task learning.
Executive Summary
This article investigates the trade-offs among ensembling, merging, and routing techniques for combining the outputs of multiple language models in multi-task learning settings. The authors empirically evaluate the performance of these methods and propose expert selection techniques to mitigate the computational cost of routing. Their findings indicate that routing offers the greatest gains in performance, but with increased complexity. The article contributes to the understanding of model fusion in multi-task learning, a critical area of research in natural language processing.
Key Points
- ▸ Ensembling, merging, and routing are three main strategies for combining the outputs of multiple language models in multi-task learning settings.
- ▸ The authors empirically evaluate the performance of these methods and identify their trade-offs.
- ▸ Expert selection techniques, such as clustering and greedy subset selection, can mitigate the computational cost of routing.
Merits
Advancing Model Fusion Research
The article contributes to the understanding of model fusion in multi-task learning, a critical area of research in natural language processing.
Practical Applications
The expert selection techniques proposed in the article can be applied in real-world settings to improve the performance of multi-task learning systems.
Demerits
Limited Generalizability
The article's findings may not generalize to other domains or tasks, limiting the broader applicability of the results.
High Computational Complexity
The routing method, despite its potential benefits, imposes significant computational overhead, which may be a barrier to adoption in practice.
Expert Commentary
The article provides a thorough investigation of the trade-offs among ensembling, merging, and routing techniques for combining the outputs of multiple language models in multi-task learning settings. The authors' findings are well-supported by empirical evidence and contribute to the understanding of model fusion in natural language processing. However, the article's limitations, particularly the potential for high computational complexity, should be carefully considered in practice. Additionally, the article's focus on multi-task learning is closely related to transfer learning, a key area of research in deep learning. The article's implications for explainability and interpretability in deep learning are also noteworthy.
Recommendations
- ✓ Future research should investigate the application of the expert selection techniques proposed in the article to other domains and tasks.
- ✓ Further work is needed to develop methods for explaining and interpreting the results of multi-task learning systems, which are critical for their adoption in practice.