The emotions we experience involve complex processes; besides physiological aspects, research in psychology has studied cognitive appraisals where people assess their situations subjectively, according to their own values (Scherer, 2005). Thus, the same situation can often result in different emotional experiences. While the detection of emotion is a well-established task, there is very limited work so far on the automatic prediction of cognitive appraisals. This work fills the gap by presenting CovidET-Appraisals, the most comprehensive dataset to-date that assesses 24 appraisal dimensions, each with a natural language rationale, across 241 Reddit posts. CovidET-Appraisals presents an ideal testbed to evaluate the ability of large language models -- excelling at a wide range of NLP tasks -- to automatically assess and explain cognitive appraisals. We found that while the best models are performant, open-sourced LLMs fall short at this task, presenting a new challenge in the future development of emotionally intelligent models. We release our dataset at https://github.com/honglizhan/CovidET-Appraisals-Public.

心理学研究中关于情感的复杂过程涉及生理和认知评估方面；虽然情境可以导致不同的情感经验，但是对于认知评估的自动预测却没有多少研究。本研究填补了这一空白，提出了CovidET-Appraisals数据集，该数据集是迄今为止最全面的数据集，对241个Reddit帖子中的24个评估维度进行了评估，并提供了自然语言的解释。CovidET-Appraisals是评估大型语言模型在自动评估和解释认知评估方面能力的理想测试平台。研究结果显示，在这一任务上，尽管最好的模型表现出色，但开源的大型语言模型在此任务中表现不佳，为情感智能模型的未来发展提出了新的挑战。我们在此链接分享了我们的数据集 https URL。

大型语言模型评估情绪的主观认知评估