1,083 research outputs found
Causal interventions expose implicit situation models for commonsense language understanding
Accounts of human language processing have long appealed to implicit
``situation models'' that enrich comprehension with relevant but unstated world
knowledge. Here, we apply causal intervention techniques to recent transformer
models to analyze performance on the Winograd Schema Challenge (WSC), where a
single context cue shifts interpretation of an ambiguous pronoun. We identify a
relatively small circuit of attention heads that are responsible for
propagating information from the context word that guides which of the
candidate noun phrases the pronoun ultimately attends to. We then compare how
this circuit behaves in a closely matched ``syntactic'' control where the
situation model is not strictly necessary. These analyses suggest distinct
pathways through which implicit situation models are constructed to guide
pronoun resolution.Comment: Findings of AC
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