arXiv:2507.10599v2 Announce Type: replace Abstract: As large language models (LLMs) increasingly power conversational agents, understanding how they model users' emotional states is critical for ethical deployment. Inspired by emotion wheels, i.e., a psychological framework that argues emotions organize hierarchically, we analyze probabilistic dependencies between emotional states in model outputs. We find that LLMs naturally form hierarchical emotion trees that align with human psychological models, and larger models develop more complex hierarchies. We also uncover systematic biases in emoti
The increasing sophistication and widespread deployment of large language models necessitates a deeper understanding of their internal mechanisms, particularly regarding human-like attributes like emotion modeling.
Understanding how LLMs process and represent emotions is crucial for developing ethical, empathetic, and effective AI agents, directly impacting user interaction and trust.
This research provides a foundational insight into the emergent properties of LLMs, revealing an intrinsic ability to form complex emotion hierarchies, which was previously an assumption or a desired outcome.
- · AI ethics researchers
- · Conversational AI developers
- · Psychology-informed AI design
- · AI safety organizations
- · Companies ignoring AI emotional biases
- · Developers deploying emotionally unskilled LLMs
LLMs can be engineered with more nuanced emotional intelligence based on these emergent hierarchies.
This could lead to more persuasive or manipulative AI if not carefully governed, necessitating robust bias detection and mitigation strategies.
The ability of LLMs to model complex emotional states may eventually enable new forms of human-AI collaboration that leverage emotional understanding.
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