Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization
arXiv:2607.00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention module for in-context learning. With richer context, transformers adapt more effectively to the current use case without any parameter updates. However, the quadratic computational and memory complexity with respect to context length significantly slows data processing in softmax transformers. Linear transformers were proposed to address this issue by reducing the complexity to linear dependence on co
The continuous growth in large language model size and context windows necessitates more efficient architectures to maintain computational feasibility.
Improving the efficiency of transformers directly impacts the scalability and cost-effectiveness of advanced AI models, making them more accessible and powerful.
This research suggests a path to more computationally efficient transformer models, allowing for richer context without prohibitive resource demands.
- · AI developers
- · Cloud computing providers
- · AI-driven industries
- · Inefficient AI architectures
- · Companies reliant on older, less optimized models
- · Hardware providers without efficient accelerators
More powerful and scalable AI models become feasible for a wider range of applications.
Reduced computational costs foster increased innovation and deployment of AI solutions across various sectors.
The democratization of advanced AI could accelerate the development of autonomous systems and agents.
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Read at arXiv cs.LG