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Recently, large pre-trained language models (LLMs) have demonstrated superiorlanguage understanding abilities, including zero-shot causal reasoning.However, it is unclear to what extent their capabilities are similar to humanones. We here study the processing of an event $B$ in a script-based story,which causally depends on a previous event $A$. In our manipulation, event $A$is stated, negated, or omitted in an earlier section of the text. We firstconducted a self-paced reading experiment, which showed that humans exhibitsignificantly longer reading times when causal conflicts exist ($\neg A\rightarrow B$) than under logical conditions ($A \rightarrow B$). However,reading times remain similar when cause A is not explicitly mentioned,indicating that humans can easily infer event B from their script knowledge. Wethen tested a variety of LLMs on the same data to check to what extent themodels replicate human behavior. Our experiments show that 1) only recent LLMs,like GPT-3 or Vicuna, correlate with human behavior in the $\neg A \rightarrowB$ condition. 2) Despite this correlation, all models still fail to predictthat $nil \rightarrow B$ is less surprising than $\neg A \rightarrow B$,indicating that LLMs still have difficulties integrating script knowledge. Ourcode and collected data set are available athttps://github.com/tony-hong/causal-script.

Do large language models and humans have similar behaviors in causal inference with script knowledge?