Academically intelligent LLMs are not necessarily socially intelligent
This work addresses the gap in evaluating social intelligence for LLMs, which is important for developers and researchers aiming to improve AI's social capabilities, though it is incremental as it builds on existing human frameworks.
The authors tackled the problem of assessing social intelligence in large language models (LLMs) by developing a standardized test called SESI based on real-world scenarios, and found that LLMs' social intelligence has significant room for improvement with a low correlation to academic intelligence.
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.