Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities

• URL: http://arxiv.org/abs/2503.22517v2
• Date: Tue, 01 Apr 2025 10:42:11 GMT
• Title: Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities
• Authors: Raman Dutt, Harleen Hanspal, Guoxuan Xia, Petru-Daniel Tudosiu, Alexander Black, Yongxin Yang, Steven McDonagh, Sarah Parisot,
• Abstract summary: We exploit the redundancy within Mixture-of-Experts (MoEs) as a source of additional capacity for learning a new modality.<n>We preserve the original language generation capabilities by applying low-rank adaptation exclusively to the tokens of the new modality.
• Score: 69.26544016976396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we undertake the challenge of augmenting the existing generative capabilities of pre-trained text-only large language models (LLMs) with multi-modal generation capability while satisfying two core constraints: C1 preserving the preservation of original language generative capabilities with negligible performance degradation, and C2 adhering to a small parameter budget to learn the new modality, ensuring scalability and efficiency. In contrast to current approaches that add dedicated modules, thereby significantly increasing the parameter count, we propose a method that leverages the underutilized capacity inherent in deep models. Specifically, we exploit the parameter redundancy within Mixture-of-Experts (MoEs) as a source of additional capacity for learning a new modality, enabling better parameter efficiency (C1). Moreover, we preserve the original language generation capabilities by applying low-rank adaptation exclusively to the tokens of the new modality (C2). Furthermore, we introduce a novel parameter initialization scheme based on the Gromov-Wasserstein distance to improve convergence and training stability. Through an extensive analysis of the routing mechanism, we uncover the emergence of modality-specific pathways and decreased redundancy within the experts that can efficiently unlock multi-modal generative capabilities. Overall, our method can be seamlessly applied to a wide range of contemporary LLMs, providing a new pathway for transitioning from uni-modal to multi-modal architectures.

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