Researchers propose attention-free model mixing with autoencoders for masked language tasks
A new paper on arXiv explores an alternative to attention mechanisms in transformer-based masked language models. The authors introduce a method using autoencoder-based mixing modules to replace attention, reducing computational costs by about 1.9 times. Their approach includes local, full-sequence, and attention-head-specific layers, each with a low-rank bottleneck. For masked positions, they…
Key points
- Autoencoder-based modules replace attention in masked language models, cutting FLOPs by ~1.9x
- Iterative refinement refines masked embeddings via neighbor averaging and manifold projection
- Matches BERT/TinyBERT on rare-token tasks with frequency-aware masking, per authors
The method matches parameter-matched BERT and TinyBERT baselines on rare-token tasks, using a frequency-aware training schedule. The paper claims the architecture achieves comparable performance to attention at lower FLOPs, though no external validation or benchmarking is provided.
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