Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers (2311.10642v4)

Abstract: This work presents an analysis of the effectiveness of using standard shallow feed-forward networks to mimic the behavior of the attention mechanism in the original Transformer model, a state-of-the-art architecture for sequence-to-sequence tasks. We substitute key elements of the attention mechanism in the Transformer with simple feed-forward networks, trained using the original components via knowledge distillation. Our experiments, conducted on the IWSLT2017 dataset, reveal the capacity of these "attentionless Transformers" to rival the performance of the original architecture. Through rigorous ablation studies, and experimenting with various replacement network types and sizes, we offer insights that support the viability of our approach. This not only sheds light on the adaptability of shallow feed-forward networks in emulating attention mechanisms but also underscores their potential to streamline complex architectures for sequence-to-sequence tasks.

• The paper demonstrates that shallow feed-forward networks, replacing attention layers via knowledge distillation, achieve competitive BLEU scores on IWSLT2017 translation tasks.
• It systematically compares several replacement strategies—ALR, ALRR, ASLR, and ELR—to assess their impact on performance and architectural efficiency.
• The study reveals that while FF networks can streamline model complexity and reduce parameter counts, they struggle with replicating effective cross-attention functionality.

Analysis of Shallow Feed-Forward Networks as Substitutes for Attention in Transformers

The research conducted by Bozic et al. embarks on a critical analysis of substituting the attention mechanism in Transformer architectures with shallow feed-forward (FF) networks, aiming to assess the performance and viability of such transformations in sequence-to-sequence tasks. This investigation is premised on the successful implementation of these FF networks, leveraging knowledge distillation from traditional attention mechanisms without significantly degrading the performance metrics, primarily the BLEU score, on the IWSLT2017 language translation tasks.

Methodology

The research employs a systematic approach whereby various methodologies for replacing attention layers with FF networks are explored. The primary configurations include:

1. Attention Layer Replacement (ALR) - Substitutes the multi-head attention block while maintaining the residual connections.
2. Attention Layer with Residual Connection Replacement (ALRR) - Replaces both the multi-head attention and its residual connection.
3. Attention Separate Heads Layer Replacement (ASLR) - Each attention head is individually replaced with a distinct FF network.
4. Encoder Layer Replacement (ELR) - The entire encoder layer is substituted by an FF network.

Each replacement approach is executed in varying configurations and sizes ranging from XS to L, with comprehensive evaluation against the standard Transformer model serving as the baseline.

Key Findings

The results from these experiments underline the potential of shallow FF networks to successfully emulate the self-attention mechanisms of Transformers. The ALR, identified as a high-performing replacement strategy, achieves relative parity with the baseline Transformer in terms of BLEU scores while hinting at streamlined capacity requirements by reducing parameter counts despite a fixed sequence length. However, challenges are noted in replicating cross-attention functionality, where performance losses were more significant, underscoring the complexity of inter-sequence interactions that FF networks struggled to capture.

The substitution experiments, particularly at full Transformer replacements, reveal critical distinctions with the cross-attention module. While they expose shortcomings in replacing the cross-attention module outright, they also illuminate pathways for developing more sophisticated FF network designs future work could potentially explore.

Implications and Speculation

The implications of these findings are multifaceted. The potential to reduce the complexity and improve efficiency in sequence-to-sequence models holds appeal for real-world applications where resource constraints are pivotal. Furthermore, introducing knowledge distillation as a viable tool in training less intuitive architectures raises questions about the dependencies and structure of model efficiency.

From a theoretical standpoint, these insights contribute to ongoing discourse about the architectural nature and necessity of key components like attention in Transformers. This paper’s rigorous ablation studies indicate a nuanced landscape where architectural sophistication does not necessarily equate to performance superiority but may instead point to a field of unexplored design flexibility.

Conclusion

In conclusion, Bozic et al. have successfully elucidated both the capabilities and limitations of shallow FF networks as an alternative to traditional attention mechanisms in Transformers. While the paper exposes particular challenges, especially concerning cross-attention, it opens a compelling dialogue on optimizing sequence-to-sequence models in machine learning. The paper suggests a promising field of exploration for future investigations involving the optimization of FF networks and enhancing our understanding of network architectures beyond current conventions.