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what is transformer architecture

7 cited papers · May 25, 2026 · Powered by Researchly AI

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The Transformer is a neural network architecture based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. 📄 Its particular a…

The Transformer is a neural network architecture based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. 📄 Vaswani et al. (2017) Its particular algebraic form arises from projection principles rooted in distributional semantics, where the query-key-value mechanism emerges as a natural asymmetric extension for modeling directional relationships. 📄 Mehta (2025)12
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Attention Is All You NeedAshish Vaswani, Noam Shazeer et al.2017Advances in Neural Information Processing Systems (NeurIPS)
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2
Self-Attention as Distributional Projection: A Unified Interpretation of Transformer ArchitectureNihal Mehta2025arXiv
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  • Self-Attention Mechanism — Projects corpus-level co-occurrence statistics into sequence context; the query-key-value mechanism captures contextual influence and directional relationships between tokens. 📄 Mehta (2025)
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  • Multi-Head Attention (MHA) — Runs multiple attention operations in parallel, where specialized heads play consistent and often linguistically-interpretable roles; the vast majority of heads can be pruned without seriously affecting performance. 📄 Voita et al. (2019)
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  • Skip Connections & MLPs — Critical structural components that prevent self-attention output from degenerating; without them, output converges doubly exponentially to a rank-1 matrix. 📄 Dong et al. (2021)
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  • Positional Encodings — Structured refinements of the same projection principle underlying self-attention, enabling the model to capture token order information. 📄 Mehta (2025)
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Self-Attention as Distributional Projection: A Unified Interpretation of Transformer ArchitectureNihal Mehta2025arXiv
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Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be PrunedElena Voita, David Talbot et al.2019OpenAlex
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3
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with DepthYihe Dong, Jean-Baptiste Cordonnier et al.2021Semantic Scholar
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Diagram
INPUT SEQUENCE (tokens)
 |
 v
+-------------------+
| Input Embedding | [vocab_size → d_model]
+-------------------+
 |
 v
+-------------------+
| Positional | [adds position info to embeddings]
| Encoding |
+-------------------+
 |
 v
========== ENCODER STACK (N layers) ==========
| |
| +---------------------------------------+ |
| | Multi-Head Self-Attention | |
| | Q = XW_Q K = XW_K V = XW_V | |
| | head_i = Attention(Q_i, K_i, V_i) | |
| | Attention = softmax(QK^T/√d_k)·V | |
| +---------------------------------------+ |
| | |
| [Add & Norm] ← skip connection |
| | |
| +---------------------------------------+ |
| | Feed-Forward Network (MLP) | |
| | FFN(x) = max(0, xW_1+b_1)W_2+b_2 | |
| +---------------------------------------+ |
| | |
| [Add & Norm] ← skip connection |
==============================================
 |
 v
 ENCODER OUTPUT
 (contextual representations)
 |
 v
========== DECODER STACK (N layers) ==========
| |
| +---------------------------------------+ |
| | Masked Multi-Head Self-Attention | |
| | (prevents attending to future tokens) | |
| +---------------------------------------+ |
| | |
| [Add & Norm] ← skip connection |
| | |
| +---------------------------------------+ |
| | Cross-Attention | |
| | Q ← Decoder, K/V ← Encoder Output | |
| +---------------------------------------+ |
| | |
| [Add & Norm] ← skip connection |
| | |
| +---------------------------------------+ |
| | Feed-Forward Network (MLP) | |
| +---------------------------------------+ |
| | |
| [Add & Norm] ← skip connection |
==============================================
 |
 v
+-------------------+
| Linear + Softmax | [d_model → vocab_size]
+-------------------+
 |
 v
OUTPUT PROBABILITIES (next token prediction)
The quadratic computational complexity of Multi-Head Attention (MHA) with respect to sequence length presents a significant barrier to scaling, particularly for long-context applications. 📄 Filipek (2025) To address this, approaches like Sparse Query Attention (SQA) reduce the number of Query heads rather than Key/Value heads, directly decreasing FLOPs for attention score computation — an optimization path complementary to Multi-Query Attention (MQA) and Grouped-Query Attention (GQA). 📄 Filipek (2025) Beyond parallelism, multi-head attention can be reframed as a system of synergistic computational graphs where each head functions as a feedforward directed acyclic graph (DAG), yielding faster mixing times and minimax fidelity amplification under specific head-diversity conditions. 📄 Borde (2025)12
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Sparse Query Attention (SQA): A Computationally Efficient Attention Mechanism with Query Heads ReductionAdam Filipek2025arXiv
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2
Beyond Parallelism: Synergistic Computational Graph Effects in Multi-Head AttentionHaitz Sáez de Ocáriz Borde2025arXiv
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  • Rank Collapse — Without skip connections or MLPs, self-attention output converges doubly exponentially to a rank-1 matrix, meaning pure attention alone is insufficient for deep networks. 📄 Dong et al. (2021)
1
  • Attention Sink — In Vision Transformers, an excessive amount of attention is allocated to the [CLS] token, distorting the model's ability to effectively process image patches. 📄 Feng et al. (2025)
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Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with DepthYihe Dong, Jean-Baptiste Cordonnier et al.2021Semantic Scholar
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EDIT: Enhancing Vision Transformers by Mitigating Attention Sink through an Encoder-Decoder ArchitectureWenfeng Feng, Hongxiang Wang et al.2025arXiv
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  • The Transformer is built solely on attention mechanisms, eliminating recurrence and convolutions. 📄 Vaswani et al. (2017)
1
  • The query-key-value mechanism arises naturally from distributional projection principles, not arbitrary design. 📄 Mehta (2025)
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  • Specialized attention heads carry most of the representational load; 38 out of 48 encoder heads can be pruned with only a 0.15 BLEU drop. 📄 Voita et al. (2019)
3
  • Skip connections and MLPs are essential — without them, attention outputs degenerate doubly exponentially. 📄 Dong et al. (2021)
4
  • Multi-head attention provides synergistic benefits beyond parallelism, enhancing information propagation across the network. 📄 Borde (2025)
5
1
Attention Is All You NeedAshish Vaswani, Noam Shazeer et al.2017Advances in Neural Information Processing Systems (NeurIPS)
View
2
Self-Attention as Distributional Projection: A Unified Interpretation of Transformer ArchitectureNihal Mehta2025arXiv
View
3
Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be PrunedElena Voita, David Talbot et al.2019OpenAlex
View
4
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with DepthYihe Dong, Jean-Baptiste Cordonnier et al.2021Semantic Scholar
5
Beyond Parallelism: Synergistic Computational Graph Effects in Multi-Head AttentionHaitz Sáez de Ocáriz Borde2025arXiv
View
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  1. "How does scaled dot-product attention work mathematically in Transformers?"
  2. "BERT vs GPT vs T5: how do encoder-only, decoder-only, and encoder-decoder Transformers differ?"
  3. "Efficient Transformer variants for long sequences: Longformer, Linformer, and FlashAttention"

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