Here we present an alternative computational approach where event boundaries are derived using a large language model, GPT-3, instead of using human annotations.  We demonstrate that GPT-3 can segment continuous narrative text into events.  GPT-3-annotated events are significantly correlated with human event annotations.  Furthermore, these GPT-derived annotations achieve a good approximation of the "consensus" solution (obtained by averaging across human annotations); the boundaries identified by GPT-3 are closer to the consensus, on average, than boundaries identified by individual human annotators.  This finding suggests that GPT-3 provides a feasible solution for automated event annotations, and it demonstrates a further parallel between human cognition and prediction in large language models.  In the future, GPT-3 may thereby help to elucidate the principles underlying human event perception.
GPT-3
Here we present an alternative computational approach where event boundaries are derived using a large language model, GPT-3, instead of using human annotations.  We demonstrate that GPT-3 can segment continuous narrative text into events.  GPT-3-annotated events are significantly correlated with human event annotations.  Furthermore, these GPT-derived annotations achieve a good approximation of the "consensus" solution (obtained by averaging across human annotations); the boundaries identified by GPT-3 are closer to the consensus, on average, than boundaries identified by individual human annotators.  This finding suggests that GPT-3 provides a feasible solution for automated event annotations, and it demonstrates a further parallel between human cognition and prediction in large language models.  In the future, GPT-3 may thereby help to elucidate the principles underlying human event perception.
GPT-3
Here we present an alternative computational approach where event boundaries are derived using a large language model, GPT-3, instead of using human annotations.  We demonstrate that GPT-3 can segment continuous narrative text into events.  GPT-3-annotated events are significantly correlated with human event annotations.  Furthermore, these GPT-derived annotations achieve a good approximation of the "consensus" solution (obtained by averaging across human annotations); the boundaries identified by GPT-3 are closer to the consensus, on average, than boundaries identified by individual human annotators.  This finding suggests that GPT-3 provides a feasible solution for automated event annotations, and it demonstrates a further parallel between human cognition and prediction in large language models.  In the future, GPT-3 may thereby help to elucidate the principles underlying human event perception.
GPT-3