Situational Evaluation for Social Intelligence of Large Language Models
Abstract: 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 for Social Intelligence (SESI).
We conducted an extensive evaluation with 13 popular and state-of-art LLMs 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.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: corpus creation, benchmarking, language resources, automatic creation and evaluation of language resources, NLP datasets, automatic evaluation of datasets, evaluation methodologies, evaluation, metrics
Contribution Types: Model analysis & interpretability, Data resources, Data analysis
Languages Studied: English
Submission Number: 3290
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