测试大模型在对话中表达情绪的能力，发现表现差异大且难维持极端情绪。
Don't Get Too Excited -- Eliciting Emotions in LLMs
- 用多轮对话模拟+情感分析评估模型情绪表达范围。
- 部分模型情绪波动稳定，但多数难以持续表达强烈情绪。
- 适合研究具身智能或情感交互的开发者参考。
本文研究大语言模型（LLMs）在长时间对话中控制情绪表达的挑战，重点考察其在唤醒度（arousal）和效价（valence）维度上的情感表达能力。通过结合基于LLM的情感分析与多轮对话模拟，量化评估了主流开源大模型在情感谱系中的表现及其互动过程中的波动情况。结果显示，不同模型在保持情绪一致性方面存在显著差异，部分模型表现出更稳定的动态轨迹；同时，模型在生成和维持极端情绪状态以及根据对话上下文灵活调整情绪方面仍存在明显局限。这些发现对构建更具情感智能的AI系统具有重要意义，并凸显了当前大模型在情感建模方面的不足。
原文摘要 · Abstract (English)
This paper investigates the challenges of affect control in large language models (LLMs), focusing on their ability to express appropriate emotional states during extended dialogues. We evaluated state-of-the-art open-weight LLMs to assess their affective expressive range in terms of arousal and valence. Our study employs a novel methodology combining LLM-based sentiment analysis with multiturn dialogue simulations between LLMs. We quantify the models' capacity to express a wide spectrum of emotions and how they fluctuate during interactions. Our findings reveal significant variations among LLMs in their ability to maintain consistent affect, with some models demonstrating more stable emotional trajectories than others. Furthermore, we identify key challenges in affect control, including difficulties in producing and maintaining extreme emotional states and limitations in adapting affect to changing conversational contexts. These findings have important implications for the development of more emotionally intelligent AI systems and highlight the need for improved affect modelling in LLMs.
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