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Raw abstract
Recent advances in large-scale neural architectures have enabled remarkable improvements across NLP tasks. However, the computational demands of these models remain a significant barrier to adoption in real-world settings. We propose SPARSE-ATTN, a novel attention mechanism that reduces quadratic complexity to near-linear through dynamic token pruning guided by a lightweight learned gating network. Experiments on GLUE and SQuAD 2.0 demonstrate 97.3% performance retention with 62% fewer FLOPs. We identify three key failure modes under distribution shift and discuss implications for deployment in resource-constrained environments.
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Problem
High computational cost blocks real-world adoption of large neural models.
Core Mechanism
Dynamic token pruning reduces attention complexity from O(n²) to near-linear.
Failure Modes
Performance degrades under distribution shift in real-world deployment.