SpectR: Dynamically Composing LM Experts with Spectral Routing
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911106511929344 |
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| author | Fleshman, William Van Durme, Benjamin |
| author_facet | Fleshman, William Van Durme, Benjamin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SpectR: Dynamically Composing LM Experts with Spectral Routing Fleshman, William Van Durme, Benjamin Computation and Language Artificial Intelligence Machine Learning 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. |
| title | SpectR: Dynamically Composing LM Experts with Spectral Routing |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.03454 |