Summary of Llms Can Evolve Continually on Modality For X-modal Reasoning, by Jiazuo Yu et al.
LLMs Can Evolve Continually on Modality for X-Modal Reasoning
by Jiazuo Yu, Haomiao Xiong, Lu Zhang, Haiwen Diao, Yunzhi Zhuge, Lanqing Hong, Dong Wang, Huchuan Lu, You He, Long Chen
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes PathWeave, a flexible and scalable framework for Multimodal Large Language Models (MLLMs) to continually learn on new modalities. The existing methods rely heavily on extensive pretraining and tuning, which can be computationally burdensome when expanding to new modalities. PathWeave leverages the concept of Continual Learning and introduces an incremental training strategy atop pre-trained MLLMs, enabling expansion to new modalities using uni-modal data without joint-modal pretraining. The framework consists of a novel Adapter-in-Adapter (AnA) architecture that seamlessly integrates uni-modal and cross-modal adapters for efficient modality alignment and collaboration. To evaluate the proposed method, a challenging benchmark called Continual Learning of Modality (MCL) is established, consisting of high-quality QA data from five distinct modalities: image, video, audio, depth, and point cloud. The experiments demonstrate the effectiveness of PathWeave in learning plasticity and memory stability during continual learning, while reducing parameter training burdens by 98.73%. The proposed method outperforms state-of-the-art MLLMs while achieving comparable results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PathWeave is a new way for computers to learn about different types of data, like pictures, videos, or sounds. Right now, these machines need a lot of training to understand all this information. PathWeave makes it possible for them to learn gradually and adapt to new types of data without needing so much training. This helps computers become better at understanding many different things at once. The researchers tested their method on a special dataset with questions and answers from five different areas: images, videos, audio, depth, and point cloud. Their results show that PathWeave works well and is efficient, reducing the need for training by 98.73%. |
Keywords
» Artificial intelligence » Alignment » Continual learning » Pretraining
