arXiv:2601.06870v2 Announce Type: replace Abstract: Multimodal large language models have demonstrated strong ability in capturing semantic representations for multimodal sentiment analysis. Their capacity to learn stable and generalizable multimodal features is limited, however, by the scarcity of high-quality training data. To address this, we propose QASA (Quality-Aware Semantic Augmentation), which uses diffusion models to generate augmented visual and auditory samples, thereby enlarging the training dataset and supporting multimodal learning. The generated samples can vary in quality and
The increasing sophistication of generative AI, specifically diffusion models, allows for the creation of high-quality synthetic data necessary to address the data scarcity prevalent in training multimodal large language models.
This research provides a method to overcome a major limitation in multimodal AI development, potentially accelerating advancements in sentiment analysis and other complex AI applications that rely on diverse data inputs.
The ability to synthetically generate high-quality training data for multimodal AI shifts the bottleneck from data acquisition to data generation and quality control, enabling faster iteration and development.
- · AI researchers
- · Generative AI developers
- · Multimodal LLM providers
- · Data augmentation platforms
- · Organizations reliant on manual data collection
- · Less sophisticated data annotation services
Multimodal AI systems will become more robust and performant due to larger and higher-quality training datasets.
The cost and time required to develop advanced multimodal AI applications will decrease, broadening access and application areas.
Ethical considerations around synthetic data, including bias propagation and provenance, will become more prominent research and regulatory areas.
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Read at arXiv cs.LG