Bench-CoE: a Framework for Collaboration of Experts from Benchmark (2412.04167v1)

Abstract: LLMs are key technologies driving intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven experts with diverse capabilities have been developed, accompanied by corresponding benchmarks to evaluate their performance. This paper proposes the Bench-CoE framework, which enables Collaboration of Experts (CoE) by effectively leveraging benchmark evaluations to achieve optimal performance across various tasks. Bench-CoE includes a set of expert models, a router for assigning tasks to corresponding experts, and a benchmark dataset for training the router. Moreover, we formulate Query-Level and Subject-Level approaches based on our framework, and analyze the merits and drawbacks of these two approaches. Finally, we conduct a series of experiments with vary data distributions on both language and multimodal tasks to validate that our proposed Bench-CoE outperforms any single model in terms of overall performance. We hope this method serves as a baseline for further research in this area. The code is available at \url{https://github.com/ZhangXJ199/Bench-CoE}.

• The paper introduces Bench-CoE, a framework that optimizes LLM performance by coordinating expert models via benchmark-trained routing.
• It details two routing strategies—query-level for precise assignments and subject-level for robust generalizability—to handle diverse tasks.
• Experimental results demonstrate improved in-distribution accuracy and enhanced adaptability to out-of-distribution data.

Overview of "Bench-CoE: A Framework for Collaboration of Experts from Benchmark"

The paper "Bench-CoE: a Framework for Collaboration of Experts from Benchmark" designates a framework aimed at optimizing the performance of LLMs through expert collaboration. The Bench-CoE framework seeks to enhance the efficacy of LLMs in handling diverse tasks by leveraging benchmark evaluations, organizing a collaboration schema known as Collaboration of Experts (CoE). The construct focuses on assembling a consortium of expert models, which are further orchestrated by a router trained using benchmark datasets, optimizing task-specific performance.

Core Framework

The Bench-CoE leverages a set of expert models to facilitate task assignment through a centrally trained router. This router, taught with benchmark datasets, enables systematic routing of tasks to the most proficient expert model for precise query handling. The framework proposes two distinct routing strategies:

1. Query-Level Approach: This technique evaluates performance of expert models per each input query enabling a fine-grained yet computationally demanding routing process. The acquisition of labels for router training in this approach involves intensive computation, which may not generalize effectively to tasks outside of the training dataset.
2. Subject-Level Approach: In contrast, the subject-level routing empowers the router with broader labels based on the performance of expert models over subject areas, rather than individual queries. This approach not only simplifies label creation but also augurs better generalization for out-of-distribution data while lessening computational requisites.

Experiments and Results

The experimental setup spans both language and multimodal tasks with varying distribution scenarios to validate the proposed framework. The results indicate that Bench-CoE consistently outperforms isolated models, notably the query-level method excels with in-distribution data, albeit its proclivity for overfitting. However, the subject-level routing showcases superior generalizability and robustness across different data distributions without requiring extensive retraining or labeling. For instance, in language task experiments involving the MMLU-Pro dataset, Bench-CoE showed a notable performance increment over individual models, particularly within in-distribution settings.

Implications and Future Directions

The Bench-CoE framework presents substantial implications for enhancing the operational capabilities of LLMs across a breadth of challenges both in language and multimodal domains. By efficiently coordinating the strengths of specialized models, Bench-CoE provides a potent mechanism for achieving adaptable and high-performing AI systems.

Looking forward, the scalability and adaptability of Bench-CoE must be further examined, especially as it relates to dynamically incorporating evolving LLMs and benchmarks. Future work could explore more sophisticated routing mechanisms to handle capability diversity among candidate LLMs more effectively, as well as improve the model's adaptability to dynamic datasets and new models through real-time updates.

Conclusion

In summation, the paper "Bench-CoE: a Framework for Collaboration of Experts from Benchmark" is an insightful contribution to the field of AI, elucidating approaches for optimizing LLM collaboration leveraging benchmark evaluations. By presenting efficient routing strategies underpinned by benchmark-based training, Bench-CoE enhances task performance across diversified tasks, establishing a coherent baseline for future exploration in expert model collaboration and routing mechanisms. As AI continues evolving, frameworks like Bench-CoE will be pivotal in navigating the complexities inherent in the integration and practical deployment of large language and multimodal models.