基于知识型大语言模型的情感识别深度分析
In-Depth Analysis of Emotion Recognition through Knowledge-Based Large Language Models
AI总结：
本文提出结合贝叶斯线索整合与大语言模型情境知识的方法，在囚徒困境社交任务中实现接近人类水平的情感识别，验证了情境信息对自动情感识别的关键作用。
摘要：
社交情境中的情感识别是一项复杂任务，需要整合来自面部表情和情境背景的信息。传统的自动情感识别方法侧重于去情境化信号，而近期研究强调情境在塑造情感感知中的重要性。本文通过借鉴人类情感感知的心理学理论来指导自动化方法的设计，为基于情境的情感识别这一新兴领域做出贡献。我们提出了一种将情感识别方法与贝叶斯线索整合（BCI）相结合的方法，以整合来自去情境化面部表情的情感推断和通过大语言模型推断出的情境知识。我们在囚徒困境这一社交任务中解释面部表情的背景下测试了该方法。我们的结果在一系列自动情感识别方法中明确支持了BCI。最佳的自动化方法取得了与人类观察者相当的结果，表明该方法有潜力推动情感计算领域的发展。
英文摘要：
Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.
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