Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality
arXiv:2505.18227v4 Announce Type: replace-cross Abstract: In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped to an embedding, enabling parallel attention computations while preserving the input's essential information. Due to the quadratic computational complexity of transformer self-attention mechanisms, token reduction has primarily been used as an efficiency strategy. This is especially true in single vision and language domains, where it helps balance computation
The paper identifies a crucial advancement in generative AI, moving beyond mere efficiency gains in token reduction to exploring its impact on model performance and capabilities.
This research suggests a more holistic approach to token reduction, potentially unlocking new performance benchmarks and accelerating the development of more sophisticated AI models across modalities.
The focus shifts from token reduction solely as an efficiency hack to a fundamental technique for improving generative model capabilities, impacting how inputs are processed and understood.
- · AI developers
- · Generative AI companies
- · Multimodal AI research
- · Inefficient AI architectures
More efficient and capable generative AI models become feasible.
Accelerated development of universal AI models capable of seamless multimodal understanding and generation.
Increased accessibility and deployment of advanced AI across various industries due to reduced computational overhead and enhanced performance.
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Read at arXiv cs.AI