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SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2310.11667

Summary

Benchmarks for social intelligence in AI are mostly static, and existing interactive setups tend to cover narrow tasks or lack diverse, goal-driven behavior. SOTOPIA addresses this gap. It is an open-ended environment where two agents (LLMs or humans) play characters in a shared scenario. Each agent pursues a private social goal through multi-turn exchanges that combine speech, non-verbal cues and physical actions.

The task space is built by crossing GPT-4-generated, manually checked components: 90 scenarios covering cooperative, competitive and mixed goals, plus 40 detailed characters linked by relationships. Episodes are scored with SOTOPIA-EVAL, a multi-dimensional rubric drawn from sociology, psychology and economics. Beyond goal completion, it rewards things like keeping secrets, protecting finances, preserving relationships and following social norms.

GPT-4 turns out to be a reasonable stand-in for human raters on several dimensions. Model comparisons reveal clear differences between systems. On a difficult subset, SOTOPIA-hard, even GPT-4 lags behind humans in goal completion and strategic behavior. The work offers an extensible platform for measuring and eventually training socially capable language agents.

Key points

  • The environment provides 90 scenarios and 40 characters. Each character has a name, age, occupation, Big Five-style traits, moral and Schwartz values, a decision-making style, and both public and secret information.
  • There are five relationship types: family, friend, romantic, acquaintance and stranger. These determine how much of a partner's profile an agent can see. Close ties reveal everything except secrets, acquaintances see only basic and public details, and strangers see nothing.
  • SOTOPIA-EVAL judges episodes along several social dimensions. GPT-4 used as an automatic judge agrees fairly well with humans, especially on goal completion, finances and relationships.
  • Larger LLMs generally score higher, but models are easily swayed by their partners and may reveal secrets or break social rules. In example dialogues, GPT-4 gives up on its goals more readily than humans and negotiates less strategically.
  • On SOTOPIA-hard, GPT-4's goal completion rate is significantly lower than that of human role-players.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: AI & machine learning

Cited by

123 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 121 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2310.11667 · arXiv 2310.11667

@misc{zhou2023sotopia,
  title = {{SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents}},
  author = {Zhou, Xuhui and Zhu, Hao and Mathur, Leena and Zhang, Ruohong and Yu, Haofei and Qi, Zhengyang and Morency, Louis-Philippe and Bisk, Yonatan and Fried, Daniel and Neubig, Graham and Sap, Maarten},
  year = {2023},
  doi = {10.48550/arxiv.2310.11667},
  eprint = {2310.11667},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2310.11667}
}

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