Efficient Adaptive Transformer: An Empirical Study and Reproducible Framework
This provides a reproducible framework for researchers to study adaptive transformers, but it is incremental as it combines existing techniques with modest performance gains.
The paper tackles the problem of making transformer models more efficient for latency-sensitive NLP applications by unifying three adaptive efficiency techniques into a single framework called Efficient Adaptive Transformer (EAT). The result shows that EAT achieves slightly higher accuracy than DistilBERT on SST-2, though it can increase latency in shallow models.
The Efficient Adaptive Transformer (EAT) framework unifies three adaptive efficiency techniques - progressive token pruning, sparse attention, and dynamic early exiting - into a single, reproducible architecture for input-adaptive inference. EAT provides an open-source benchmarking pipeline that automates data processing, timing, and ablation across GLUE tasks (SST-2, QQP, MNLI). Although this empirical study finds that combining these mechanisms can increase latency in shallow six-layer models, it demonstrates that EAT achieves slightly higher accuracy than the optimized DistilBERT baseline on SST-2, illustrating the potential of dynamic computation for latency-sensitive NLP. The main contribution is the open, end-to-end reproducible framework - complete with scripts, CSV logging, and analysis utilities - intended to serve as a community tool for further research on adaptive transformers.