Learning Objectives
Map constructional argument structure to the patterns visible in transformer attention distributions
Analyze multi-head attention as a computational analog to construction detection and selection
Apply Construction Grammar insights to improve syntactic parsing and natural language understanding
Identify systematically when LLMs fail on non-compositional constructions and idiomatic expressions
How transformer attention mechanisms operationalize constructional form-meaning mappings in practice.
This lesson builds on the theoretical foundations established earlier in the module and extends your understanding of how cognitive-linguistic principles apply directly to modern AI systems.
Learning Objectives
Map constructional argument structure to the patterns visible in transformer attention distributions
Analyze multi-head attention as a computational analog to construction detection and selection
Apply Construction Grammar insights to improve syntactic parsing and natural language understanding
Identify systematically when LLMs fail on non-compositional constructions and idiomatic expressions
Key Concepts
Map constructional argument structure to the patterns visible in transformer attention distributions
Analyze multi-head attention as a computational analog to construction detection and selection
Apply Construction Grammar insights to improve syntactic parsing and natural language understanding
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