The academic intelligence of large language models (LLMs) has made remarkable progress in recent times, but their social intelligence performance remains unclear. Inspired by established human social intelligence frameworks, particularly Daniel Goleman's social intelligence theory, we have developed a standardized social intelligence test based on real-world social scenarios to comprehensively assess the social intelligence of LLMs, termed as the Situational Evaluation of Social Intelligence (SESI). We conducted an extensive evaluation with 13 recent popular and state-of-art LLM agents on SESI. The results indicate the social intelligence of LLMs still has significant room for improvement, with superficially friendliness as a primary reason for errors. Moreover, there exists a relatively low correlation between the social intelligence and academic intelligence exhibited by LLMs, suggesting that social intelligence is distinct from academic intelligence for LLMs. Additionally, while it is observed that LLMs can't ``understand'' what social intelligence is, their social intelligence, similar to that of humans, is influenced by social factors.

翻译：近年来，大语言模型（LLM）的学术能力取得了显著进展，但其社交智能表现仍不明确。受人类社交智能框架（尤其是丹尼尔·戈尔曼的社交智能理论）启发，我们基于真实社交场景开发了一套标准化的社交智能测试——情境化社交智能评估（SESI），以全面评估LLM的社交智能。我们对13个近期主流及最先进的LLM智能体进行了广泛评估。结果表明，LLM的社交智能仍有显著提升空间，表面友善性是导致错误的主要因素。此外，LLM展现的社交智能与学术智能之间相关性较低，表明对LLM而言社交智能与学术智能存在本质区别。同时，尽管观察到LLM无法“理解”社交智能的内涵，但其社交智能与人类相似，会受到社会因素的影响。