Specialization of Self-Attention Heads for Linguistic Structure
Visualizations of Transformer encoder self-attention reveal that individual attention heads learn to perform distinct tasks related to the syntactic structure of a sentence. Rather than computing identical distributions, different heads—such as those observed at layer 5 of 6—exhibit specialized behaviors corresponding to specific grammatical and structural relationships.
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Prep Sessions
Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Ch.1 Transformer Architecture Fundamentals - Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Attention Visualizations and Linguistic Structure Resolution - Transformer Architecture and Large Language Model Capabilities @ University of Michigan - Ann Arbor
Learn After
Match each aspect of self-attention analysis to its corresponding description.
Order the observations made when analyzing self-attention behavior across Transformer encoder heads, from initial visualization to structural interpretation.
According to findings from self-attention visualizations, what explains why the individual heads at layer 5 exhibit non-identical distributions?
Long-Distance Dependency Tracking in Self-Attention Heads
Anaphora Resolution in Self-Attention Heads