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LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models

Paper · 2024 · Yadong Zhang, Shaoguang Mao, Tao Ge et al.

Summary

Strategic reasoning means picking actions in multi-agent settings while anticipating how other agents will react and then adjusting. It has gained attention since LLMs became capable dialogue agents, but work on it was scattered across reviews of agents, simulation and game-playing. Zhang et al. (East China Normal University and Microsoft) give the topic its own systematic survey. They argue it differs from commonsense, mathematical, symbolic or causal reasoning because the environment keeps changing and opponents' moves are uncertain. They describe it through four properties: goal orientation, interactivity, prediction and adaptability. The literature is organised by application scenarios, enhancement methods, evaluation (quantitative metrics plus qualitative capability analysis) and open challenges. An appendix formalises a strategic environment as a "GAME" tuple for LLM agents, in which actions are text outputs and information states are observable dialogue histories. The authors also compare LLMs with reinforcement learning, which previously dominated the area through heavy simulation. The main contribution is a shared taxonomy for a fast-growing, interdisciplinary field relevant to business analysis and policy making.

Key points

  • Four scenario families:
    • Societal simulation: societal behaviour (BigToM, SOTOPIA, OpenToM) and debate and negotiation (WarAgent, TRIP).
    • Economic simulation: e.g., TradingGPT, CompeteAI.
    • Game theory: matrix games (MAgIC) and repeated games (Alympics, K-Level Reasoning, γ-Bench).
    • Gaming: Werewolf, Avalon/AvalonBench, Diplomacy (Cicero, Welfare Diplomacy), poker (SuspicionAgent, PokerGPT, Agent-Pro), chess (ChessGPT), and video games (TextStarCraft II, PokéLLMon, CivRealm, DOOM).
  • Four method groups:
    • Prompt engineering: task-specific and learning-based prompting.
    • Module enhancement: memory, planning, knowledge, and search/value/belief components.
    • Theory of mind: first-order ToM and cognitive hierarchy.
    • Fine-tuning: imitation learning and reinforcement learning.
  • Formal model: a GAME is defined as ⟨N, A, H, Z, u, I⟩. Each agent maximises expected utility based on its beliefs about the strategies of the other agents.
  • LLMs vs. RL (9 criteria): LLMs are more transparent, generalise better across domains and are easier to deploy. However, they adapt poorly in real time unless they are updated. RL shows the opposite profile.
  • Excluded from scope: Generative Agents-style simulations without clear strategic interaction, and collaborative tasks that need no dynamic feedback (arXiv:2404.01230, 2024).

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

 Institutions

🏢MicrosoftRedmond · United States403 works · 162,838 citations🎓East China Normal UniversityShanghai · China73 works · 2,653 citations

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 4,145 worksReasoning · 1,948 works

Cite

Cite as Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang, Adrian de Wynter, Yan Xia, Wenshan Wu, Ting Song, Man Lan and Furu Wei. 2024. LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models. arXiv:2404.01230.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2404.01230 · arXiv 2404.01230

@misc{zhang2024llm,
  title = {{LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models}},
  author = {Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang, Adrian {de Wynter}, Yan Xia, Wenshan Wu, Ting Song, Man Lan and Furu Wei},
  year = {2024},
  doi = {10.48550/arxiv.2404.01230},
  eprint = {2404.01230},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2404.01230}
}

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