超越上下文到认知评估：情感推理作为大型语言模型的心智理论基准
Beyond Context to Cognitive Appraisal: Emotion Reasoning as a Theory of Mind Benchmark for Large Language Models
AI总结：
本研究在心智理论框架下，基于认知评估理论构建专门数据集，评估大型语言模型的前向与后向情感推理能力，发现其虽具一定推理能力但难以关联情境与特定情感，强调了引入心理学理论的必要性。
摘要：
用于情感识别任务的数据集通常包含显性线索，可用于预测文本中表达的情感。然而，一个挑战是文本有时包含具有丰富情感语义的隐性上下文线索，这需要高阶推理能力来推断情感状态，而不仅仅是所传达的情感。本研究超越了表面层次的感知特征，在心智理论（ToM）框架内，探讨大型语言模型（LLMs）如何利用上下文信息对他人情感状态进行推理。基于认知评估理论，我们策划了一个专门的ToM评估数据集1，以评估前向推理（从上下文到情感）和后向推理（从情感到推断的上下文）。我们表明，LLMs能在一定程度上进行推理，尽管它们不善于将情境结果和评估与特定情感联系起来。我们的工作强调了在情感推理背景下，在LLMs的训练和评估中引入心理学理论的必要性。
英文摘要：
Datasets used for emotion recognition tasks typically contain overt cues that can be used in predicting the emotions expressed in a text. However, one challenge is that texts sometimes contain covert contextual cues that are rich in affective semantics, which warrant higher-order reasoning abilities to infer emotional states, not simply the emotions conveyed. This study advances beyond surface-level perceptual features to investigate how large language models (LLMs) reason about others' emotional states using contextual information, within a Theory-of-Mind (ToM) framework. Grounded in Cognitive Appraisal Theory, we curate a specialized ToM evaluation dataset1 to assess both forward reasoning - from context to emotion- and backward reasoning - from emotion to inferred context. We showed that LLMs can reason to a certain extent, although they are poor at associating situational outcomes and appraisals with specific emotions. Our work highlights the need for psychological theories in the training and evaluation of LLMs in the context of emotion reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
