梳理大模型情感认知进展，助力人机交互与心理健康应用。
Recent Advancement of Emotion Cognition in Large Language Models
- 按神经学家尼塞尔的认知阶段分析情感理解方法
- 对比学习等技术提升模型情感识别与生成能力
- 适合关注人机共情与心理评估的研究者参考
大语言模型的情感认知对社交媒体、人机交互和心理健康评估等应用至关重要。本文系统综述了近期在情感分类、情感丰富响应生成及心智理论评估方面的研究进展，指出当前仍面临标注数据依赖性强、情感处理复杂等挑战。通过整合关键研究、方法、成果与资源，本文基于乌尔里克·尼塞尔的认知阶段理论进行框架化梳理，并探讨未来方向，包括无监督学习与更复杂可解释的情感认知模型。此外，对比学习等先进方法被用于增强模型情感认知能力。
原文摘要 · Abstract (English)
Emotion cognition in large language models (LLMs) is crucial for enhancing performance across various applications, such as social media, human-computer interaction, and mental health assessment. We explore the current landscape of research, which primarily revolves around emotion classification, emotionally rich response generation, and Theory of Mind assessments, while acknowledge the challenges like dependency on annotated data and complexity in emotion processing. In this paper, we present a detailed survey of recent progress in LLMs for emotion cognition. We explore key research studies, methodologies, outcomes, and resources, aligning them with Ulric Neisser's cognitive stages. Additionally, we outline potential future directions for research in this evolving field, including unsupervised learning approaches and the development of more complex and interpretable emotion cognition LLMs. We also discuss advanced methods such as contrastive learning used to improve LLMs' emotion cognition capabilities.
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