Multi-Head Attention

Multi-Head Attention extends the attention mechanism by running multiple independent attention operations in parallel, each focusing on different types of relationships and aspects of the input data. Rather than using a single attention operation that computes one set of attention weights, multi-head attention runs multiple attention heads simultaneously, enabling models to simultaneously attend to information from different representation subspaces and different positions. The outputs from all heads are concatenated and linearly transformed, producing a final combined representation that integrates insights from multiple perspectives.

Each attention head operates independently on the same input representations, computing its own queries, keys, and values through learned projection matrices. The heads process in parallel: different heads may attend to different sequence positions or emphasize different features. After computing individual attention outputs, all head outputs concatenate into a single vector, then pass through a learned linear projection that combines information from all heads. This architecture enables the model to simultaneously learn multiple complementary attention patterns rather than forcing a single attention mechanism to capture all relevant relationships.

Research reveals that different attention heads learn to capture different types of linguistic and structural relationships:

Syntactic Structure: Some heads learn to capture syntactic dependencies between words. Analysis of Transformer encoders reveals attention patterns resembling syntactic phrases, with sequences of consecutive tokens attending to the same position. Different heads capture different types of dependencies—subject-verb agreement, prepositional phrase attachment, and other grammatical relationships.

Word Alignment: In machine translation, different heads specialize in aligning different word translation pairs. Some heads focus on one-to-one alignments; others capture many-to-one or one-to-many mappings. This specialization enables the model to handle complex alignment scenarios.

Positional Relationships: Some heads focus on local context, attending primarily to nearby positions. Others focus on long-range relationships, attending to distant positions. This division enables efficient capture of both local and global structure.

Semantic Relationships: Heads can learn semantic associations beyond syntactic structure, grouping related concepts and capturing conceptual relationships that transcend surface position.

Multi-head attention provides several advantages beyond computational parallelization:

Representational Capacity: Different heads can simultaneously capture multiple types of relationships impossible for a single attention mechanism. One head might focus on syntax while another focuses on semantics, enabling richer integrated representations.

Robustness: Redundancy across heads improves robustness to noise and variations. If one head fails to capture important information, others may compensate.

Interpretability: Visualizing individual head attention patterns provides insights into what model components are doing, enabling partial interpretability of Transformer behavior.

Hierarchical Processing: Multi-head attention enables hierarchical knowledge transfer. Lower-level heads can capture local patterns while higher-level heads integrate these into global understanding.

Multi-head attention proves effective across diverse applications:

Machine Translation: Different heads align different word pairs, capturing the many-to-many alignment relationships necessary for accurate translation.

Natural Language Understanding: Tasks like question answering, semantic analysis, and text classification benefit from multiple heads simultaneously capturing syntactic, semantic, and positional information.

Speech Emotion Classification: Multi-head attention enables models to simultaneously extract emotional features across different time scales and frequency ranges.

Clinical NLP: Multi-head attention on pre-trained transformers achieves state-of-the-art results on temporal relation extraction from medical records by attending to multiple aspects of contextualized embeddings.

Hierarchical Text Classification: Multi-head attention transfers knowledge between word and sentence levels, enabling models to simultaneously solve tasks at different granularities.

Despite multi-head attention's success, research reveals significant redundancy: many heads attend to similar information, wasting model capacity. Pruning studies show models maintain performance while using only 50-75% of attention heads. Several approaches address this:

Adaptive Multi-Head Attention varies the number of active heads based on input complexity, allocating resources where needed.

Head Pruning removes redundant heads while preserving important ones, reducing parameters and computation.

Dynamic Head Importance mechanisms learn to weight different heads based on their contribution to tasks.

Grouped Multi-Head Attention reduces computational complexity by processing subgroups of positions, enabling efficient long-sequence modeling.

Mixture-of-Heads Attention expresses multi-head attention as summations, enabling selective head activation where only important heads contribute, improving efficiency.

Parameter-Efficient Multi-Head Embeddings use shared projection matrices with head-specific embeddings, dramatically reducing memory requirements while maintaining performance.

Head Composition Mechanisms enable interaction between heads beyond simple concatenation, allowing heads to influence each other's computations.

Multilingual and Multi-Domain Head Specialization learns shared and specialized heads for different languages and domains, mitigating interference between tasks.

Hierarchical Multi-Head Attention operates at multiple scales, with some heads focusing on local context and others on document-level structure.