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Humans are social beings; we pursue social goals in our daily interactions,which is a crucial aspect of social intelligence. Yet, AI systems' abilities inthis realm remain elusive. We present SOTOPIA, an open-ended environment tosimulate complex social interactions between artificial agents and evaluatetheir social intelligence. In our environment, agents role-play and interactunder a wide variety of scenarios; they coordinate, collaborate, exchange, andcompete with each other to achieve complex social goals. We simulate therole-play interaction between LLM-based agents and humans within this taskspace and evaluate their performance with a holistic evaluation frameworkcalled SOTOPIA-Eval. With SOTOPIA, we find significant differences betweenthese models in terms of their social intelligence, and we identify a subset ofSOTOPIA scenarios, SOTOPIA-hard, that is generally challenging for all models.We find that on this subset, GPT-4 achieves a significantly lower goalcompletion rate than humans and struggles to exhibit social commonsensereasoning and strategic communication skills. These findings demonstrateSOTOPIA's promise as a general platform for research on evaluating andimproving social intelligence in artificial agents.

SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents