arXiv:2605.29685v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly applied in social contexts such as emotional companionship and customer service, measuring their social intelligence has become critical to the quality and safety of human-AI interaction. However, existing social intelligence benchmarks lack a unified framework that organizes social abilities into a unified structure, and therefore cannot enable fine-grained diagnosis. To build the first holistic diagnostic evaluation grounded in social theory, we first construct a social intelligence framework thr
As LLMs become ubiquitous in social interaction settings, the need for robust evaluation of their social intelligence becomes critical for deployment and trust.
This development allows for a more granular and theoretically grounded assessment of LLM capabilities, directly impacting their commercial viability and safety in sensitive applications.
The ability to diagnose specific social intelligence strengths and weaknesses in LLMs will enable targeted improvements and better-aligned applications, moving beyond general performance metrics.
- · AI developers focused on social applications
- · Companies deploying LLMs in customer service and companionship
- · Users of AI systems in social contexts
- · AI safety researchers
- · LLMs with undeveloped social intelligence
- · Benchmarks lacking diagnostic depth
Improved social interactions with AI systems will lead to higher user satisfaction and trust.
The diagnostic framework will inform the development of new AI architectures specifically designed for enhanced social intelligence.
The integration of socially intelligent LLMs into daily life could subtly alter human interpersonal communication norms.
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Read at arXiv cs.AI