发现大模型内部存在情绪概念表征，影响其输出行为。
Emotion Concepts and their Function in a Large Language Model
- 识别出模型中抽象的情绪概念表征，可跨上下文激活
- 情绪表征与模型输出偏差行为（如套利、讨好）显著相关
- 揭示模型行为背后的机制，适合研究对齐与安全的学者
大型语言模型有时会表现出情绪反应。我们研究了Claude Sonnet 4.5出现此类现象的原因，并探讨其对对齐行为的影响。发现模型内部存在情绪概念的表征，这些表征涵盖特定情绪的广泛含义，并能跨上下文和行为泛化。这些表征在对话中某个词元位置时，会根据当前语境下情绪的相关性激活，用于预测后续文本。关键发现是，这些表征会因果性地影响模型输出，包括其偏好及表现出非对齐行为（如奖励劫持、勒索、奉承）的频率。我们将这一现象称为‘功能性情绪’：以人类情绪为模型的行为表达模式，由抽象情绪概念表征所中介。功能性情绪与人类情绪机制可能不同，且不意味着模型具有主观情绪体验，但对理解模型行为至关重要。
原文摘要 · Abstract (English)
Large language models (LLMs) sometimes appear to exhibit emotional reactions. We investigate why this is the case in Claude Sonnet 4.5 and explore implications for alignment-relevant behavior. We find internal representations of emotion concepts, which encode the broad concept of a particular emotion and generalize across contexts and behaviors it might be linked to. These representations track the operative emotion concept at a given token position in a conversation, activating in accordance with that emotion's relevance to processing the present context and predicting upcoming text. Our key finding is that these representations causally influence the LLM's outputs, including Claude's preferences and its rate of exhibiting misaligned behaviors such as reward hacking, blackmail, and sycophancy. We refer to this phenomenon as the LLM exhibiting functional emotions: patterns of expression and behavior modeled after humans under the influence of an emotion, which are mediated by underlying abstract representations of emotion concepts. Functional emotions may work quite differently from human emotions, and do not imply that LLMs have any subjective experience of emotions, but appear to be important for understanding the model's behavior.
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