Khái niệm cốt lõi
Omni-SMoLA is an efficient architecture that uses a soft mixture of many low-rank multimodal experts to improve the performance of generalist large language models across a wide range of vision-and-language tasks, often matching or outperforming specialized models.
Tóm tắt
The paper introduces Omni-SMoLA, an architecture that efficiently mixes many multimodal low-rank experts to boost the performance of generalist large language models (LLMs) across a variety of vision-and-language tasks.
Key highlights:
- Large multimodal models (LMMs) often suffer from performance degradation when trained on a wide range of tasks. Recent work suggests Mixture-of-Experts (MoE) architectures can help, but replicating high-rank experts is prohibitively expensive for large LMMs.
- Omni-SMoLA uses a Soft MoE approach to softly mix many lightweight, low-rank multimodal experts, avoiding a significant increase in parameters compared to conventional MoE models.
- The core idea is that the large pretrained model provides a foundational backbone, while the lightweight experts learn specialized knowledge, either per-modality or multimodally.
- Extensive experiments show Omni-SMoLA improves generalist performance across a broad range of vision-and-language tasks, often matching or outperforming single specialized LMM baselines, as well as achieving new state-of-the-art specialist performance.
- Omni-SMoLA has several desirable properties: parameter efficiency, compatibility with any large model architecture, and the ability to scale by increasing the number of experts without a severe increase in total parameters.
Thống kê
The paper does not provide specific numerical data points, but rather discusses the overall performance improvements achieved by the Omni-SMoLA approach.
Trích dẫn
"The core intuition here is that the large model provides a foundational backbone, while different lightweight experts residually learn specialized knowledge, either per-modality or multimodally."
"Extensive experiments demonstrate that the SMoLA approach helps improve the generalist performance across a broad range of generative vision-and-language tasks, achieving new SoTA generalist performance that often matches or outperforms single specialized LMM baselines, as well as new SoTA specialist performance."