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- W4311000438 abstract "This case study investigates the extent to which a language model (GPT-2) is able to capture native speakers' intuitions about implicit causality in a sentence completion task. We first reproduce earlier results (showing lower surprisal values for pronouns that are congruent with either the subject or object, depending on which one corresponds to the implicit causality bias of the verb), and then examine the effects of gender and verb frequency on model performance. Our second study examines the reasoning ability of GPT-2: is the model able to produce more sensible motivations for why the subject VERBed the object if the verbs have stronger causality biases? We also developed a methodology to avoid human raters being biased by obscenities and disfluencies generated by the model." @default.
- W4311000438 created "2022-12-22" @default.
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- W4311000438 date "2022-12-08" @default.
- W4311000438 modified "2023-10-17" @default.
- W4311000438 title "Implicit causality in GPT-2: a case study" @default.
- W4311000438 doi "https://doi.org/10.48550/arxiv.2212.04348" @default.
- W4311000438 hasPublicationYear "2022" @default.
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