Humans perceive discrete events such as "restaurant visits" and "train rides"
in their continuous experience. One important prerequisite for studying human
event perception is the ability of researchers to quantify when one event ends
and another begins. Typically, this information is derived by aggregating
behavioral annotations from several observers. 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.

本文介绍了一种计算机处理连续叙述文本的方法，通过使用大型语言模型 GPT-3 自动完成事件分割和注释，获得与人类注释高度相关的结果，可能为深入探究人类事件感知的原理提供有用的方法。

大型语言模型能够以人类相似的方式划分叙事事件

Large language models can segment narrative events similarly to humans

Event perception tasks such as recognizing and localizing actions in
streaming videos are essential for scaling to real-world application contexts.
We tackle the problem of learning actor-centered representations through the
notion of continual hierarchical predictive learning to localize actions in
streaming videos without the need for training labels and outlines for the
objects in the video. We propose a framework driven by the notion of
hierarchical predictive learning to construct actor-centered features by
attention-based contextualization. The key idea is that predictable features or
objects do not attract attention and hence do not contribute to the action of
interest. Experiments on three benchmark datasets show that the approach can
learn robust representations for localizing actions using only one epoch of
training, i.e., a single pass through the streaming video. We show that the
proposed approach outperforms unsupervised and weakly supervised baselines
while offering competitive performance to fully supervised approaches.
Additionally, we extend the model to multi-actor settings to recognize group
activities while localizing the multiple, plausible actors. We also show that
it generalizes to out-of-domain data with limited performance degradation.

在不需要训练标签和轮廓的情况下，通过连续的分层预测学习构建基于注意力的上下文特征，以学习在流媒体视频中本地化动作的鲁棒表示方法。