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📄 Abstract
Abstract: Transformers have dominated sequence processing tasks for the past seven
years -- most notably language modeling. However, the inherent quadratic
complexity of their attention mechanism remains a significant bottleneck as
context length increases. This paper surveys recent efforts to overcome this
bottleneck, including advances in (sub-quadratic) attention variants, recurrent
neural networks, state space models, and hybrid architectures. We critically
analyze these approaches in terms of compute and memory complexity, benchmark
results, and fundamental limitations to assess whether the dominance of
pure-attention transformers may soon be challenged.
Key Contributions
This paper surveys and critically analyzes recent efforts to overcome the quadratic complexity bottleneck of Transformer attention mechanisms. It examines alternatives like sub-quadratic attention variants, RNNs, SSMs, and hybrid architectures, assessing their compute/memory complexity and benchmark performance to determine if pure-attention Transformers will be challenged.
Business Value
Identifies more efficient and scalable model architectures, enabling the development of LLMs that can handle longer contexts and require less computational resources, thus reducing operational costs.