SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents

Published: 16 Jan 2024, Last Modified: 20 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: Social, Interaction, Agent, Social intelligence, Large Language Models, Evaluation, Theory of Mind
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TL;DR: SOTOPIA is a novel, challenging, and interactive benchmark that could serve as the perfect test-bed and potential incubator for social intelligence.
Abstract: *Humans are social beings*; we pursue social goals in our daily interactions, which is a crucial aspect of social intelligence. Yet, AI systems' abilities in this realm remain elusive. We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence. In our environment, agents role-play and *interact* under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We simulate the role-play interaction between LLM-based agents and humans within this task space and evaluate their performance with a holistic evaluation framework called SOTOPIA-Eval. With SOTOPIA, we find significant differences between these models in terms of their social intelligence, and we identify a subset of SOTOPIA scenarios, SOTOPIA-hard, that is generally challenging for all models. We find that on this subset, GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills. These findings demonstrate SOTOPIA's promise as a general platform for research on evaluating and improving social intelligence in artificial agents.
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Primary Area: datasets and benchmarks
Submission Number: 6556