SpectR: Dynamically Composing LM Experts with Spectral Routing

• URL: http://arxiv.org/abs/2504.03454v1
• Date: Fri, 04 Apr 2025 13:58:44 GMT
• Title: SpectR: Dynamically Composing LM Experts with Spectral Routing
• Authors: William Fleshman, Benjamin Van Durme,
• Abstract summary: This paper introduces SPECTR, an approach for dynamically composing expert models at each time step during inference.<n>We show that SPECTR improves routing accuracy over alternative training-free methods, increasing task performance across expert domains.
• Score: 37.969478059005574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training large, general-purpose language models poses significant challenges. The growing availability of specialized expert models, fine-tuned from pretrained models for specific tasks or domains, offers a promising alternative. Leveraging the potential of these existing expert models in real-world applications requires effective methods to select or merge the models best suited for a given task. This paper introduces SPECTR, an approach for dynamically composing expert models at each time step during inference. Notably, our method requires no additional training and enables flexible, token- and layer-wise model combinations. Our experimental results demonstrate that SPECTR improves routing accuracy over alternative training-free methods, increasing task performance across expert domains.

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