KI-Glossar
Das vollständige Wörterbuch der Künstlichen Intelligenz
Attention Head Analysis
Process of examining and interpreting the attention weights produced by each head to understand the specific patterns and relationships that each head has learned to capture.
Head Specialization
Phenomenon where different attention heads in the same layer specialize to learn distinct types of linguistic relationships, such as syntax, semantics, or long-range dependencies.
Attention Weight Matrix
Square matrix generated by an attention head, where each element (i, j) represents the importance or relevance score of token j for token i in the context of the sequence.
Attention Map
Visualization of the attention weight matrix, often in the form of a heatmap, which graphically illustrates the focus relationships of an attention head on an input sequence.
Syntactic Role
Type of relationship, such as subject-verb binding or dependency between a noun and its adjective, that a specialized attention head can learn to detect and model.
Positional Role
Function of an attention head that primarily focuses on relative positional relationships between tokens, helping the model understand word order regardless of their semantic content.
Positional Head
Attention head whose attention weights reveal patterns strongly related to the relative distance between tokens, acting as a mechanism to encode sequential structure.
Subword Head
Attention head specialized in managing relationships between word fragments (subwords) generated by tokenizers like BPE, helping to reconstruct lexical coherence.
Retrieval Head
Attention head identified in large models that behaves as an information retrieval mechanism, strongly connecting to specific tokens that act as 'keys' for memorized knowledge.
Head Redundancy
Observation that certain attention heads in an over-parameterized model learn very similar or identical functions, suggesting potential inefficiency in resource usage.
Attention Head Pruning
Model compression technique that involves identifying and removing attention heads deemed redundant or unimportant to reduce model size and computational cost with minimal impact on performance.
Head Importance Score
Quantitative metric, often derived from the sensitivity of the loss or model performance to the removal of a head, used to rank heads by their contribution to overall functioning.
Head Induction Analysis
Methodology that involves training a simple supervised model (such as a linear classifier) on the outputs of an attention head to discover the underlying function that this head has learned to represent.
Diagonal Attention Pattern
Attention weight pattern where a head focuses primarily on the token itself (self-attention), often observed in lower layers to refine local representations.
Vertical Attention Pattern
Pattern where an attention head focuses on a specific reference token (often the beginning-of-sequence token or a class marker) for all positions, aggregating information for a classification task.
Block Attention Pattern
Pattern where an attention head focuses on contiguous segments of the sequence, indicating specialization in processing local phrases or clauses.
Translation Head
In multilingual models, an attention head that learns to align words and phrases between different languages, facilitating the transfer of linguistic knowledge.
Multi-Head Attention Mechanism
Fundamental component of Transformers that executes multiple attention heads in parallel, concatenates their outputs and projects them to allow the model to focus on different positions and different representation spaces simultaneously.
Head Interpretability
Research field aimed at developing methods to understand, quantify and visualize the specific function of each attention head in order to demystify the internal workings of Transformer models.