@inproceedings{yamakoshi-etal-2023-causal,
    title = "Causal interventions expose implicit situation models for commonsense language understanding",
    author = "Yamakoshi, Takateru  and
      McClelland, James  and
      Goldberg, Adele  and
      Hawkins, Robert",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.839",
    doi = "10.18653/v1/2023.findings-acl.839",
    pages = "13265--13293",
    abstract = "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 a distinct pathway through which implicit situation models may be constructed to guide pronoun resolution",
}

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 a distinct pathway through which implicit situation models may be constructed to guide pronoun resolution
Yamakoshi, Takateru McClell , James Goldberg, Adele Hawkins, Robert
Causal interventions expose implicit situation models for commonsense language understanding