用大模型识别模糊情绪，让对话更懂人心
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models
- 设计零样本与少样本提示，利用上下文信息提升情绪识别
- 在三个数据集上验证，加上下文后识别准确率显著提升
- 首次探索大模型对复杂模糊情绪的感知能力，适合情感计算研究者
大型语言模型（LLMs）在自然语言处理任务中表现出色。除了认知智能，其情感智能潜力同样重要，有助于实现更自然、富有同理心的对话系统。现有研究多聚焦单一情绪标签，忽视人类情绪的复杂性和模糊性。本文首次探索LLMs在识别模糊情绪方面的潜力，利用其强大的泛化能力和上下文学习机制。我们设计了零样本和少样本提示，并将历史对话作为上下文信息用于情绪识别。在三个数据集上的实验表明，LLMs在识别模糊情绪方面具有显著潜力，且上下文信息带来明显收益。此外，模型在识别较不模糊情绪时表现优异，对更模糊情绪也展现出接近人类感知能力的识别潜力。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks. In addition to their cognitive intelligence, exploring their capabilities in emotional intelligence is also crucial, as it enables more natural and empathetic conversational AI. Recent studies have shown LLMs' capability in recognizing emotions, but they often focus on single emotion labels and overlook the complex and ambiguous nature of human emotions. This study is the first to address this gap by exploring the potential of LLMs in recognizing ambiguous emotions, leveraging their strong generalization capabilities and in-context learning. We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition. Experiments conducted using three datasets indicate significant potential for LLMs in recognizing ambiguous emotions, and highlight the substantial benefits of including context information. Furthermore, our findings indicate that LLMs demonstrate a high degree of effectiveness in recognizing less ambiguous emotions and exhibit potential for identifying more ambiguous emotions, paralleling human perceptual capabilities.
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