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what is transformer architecture

Generated by Researchly AI·May 25, 2026·7 sources
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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
  • 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)
1
  • 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)
2
  • 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)
3
  • Positional Encodings — Structured refinements of the same projection principle underlying self-attention, enabling the model to capture token order information. 📄 Mehta (2025)
1
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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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  • 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)
2
  • 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
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