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Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View

Paper · 2024 · Jintian Zhang, Xin Xu, Ningyu Zhang et al.

Summary

Systems built from several large language model agents are increasingly used for hard tasks, but it is unclear how their collaboration works or whether it resembles human group behaviour. The authors build a test-bed of simulated "machine societies", each with three agents. Every agent gets one of two personas, easy-going or overconfident, which yields four societies made up entirely or mostly of one trait. Over several rounds, each agent either debates with its peers or reflects on its own earlier answer, and enumerating these choices produces many collaborative strategies. The societies are tested on MATH, MMLU and Chess Move Validity, mainly with ChatGPT (GPT-3.5) and also with open models.

Some strategies outperform earlier strong methods while using fewer API tokens. Debate generally helps, the mix of personas makes little clear difference, and adding agents or rounds does not reliably improve results. Agents also show conformity and majority-driven consensus, which the authors interpret through social psychology. The study suggests that well-designed small-group collaboration may matter more than simply scaling up models or agent counts.

Key points

  • Setup: 3 agents per society, 4 societies (totally or mostly easy-going, totally or mostly overconfident), two thinking patterns (debate, reflection), and 3-round strategies giving 8 debate/reflection permutations.
  • Benchmarks: MATH, MMLU, and Chess Move Validity (from BIG-bench). Models: ChatGPT/GPT-3.5, plus Qwen 72B, LlaMA2 Chat 13B/70B and Mixtral 8×7B.
  • Efficiency: letting all agents use the same thinking pattern within a round improves efficiency. The balance between agent count and strategy matters more than scale alone.
  • Conformity: it is widespread and grows with more rounds. It helped on ChatGPT and Qwen 72B but hurt on LlaMA2 Chat and Mixtral, and its benefit fades as rounds accumulate. Debate reaches consensus more easily than reflection.
  • Resources: code and data are released at github.com/zjunlp/MachineSoM. Limitations include using a single LLM per society and offering no adaptive strategy selection.

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

📚 Shelves 5 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Psychology · 798 works Social Psychology · 129 works Developmental and Educational Psychology · 260 works

Cite

Cite as Jintian Zhang, Xin Xu, Ningyu Zhang, Ruibo Liu, Bryan Hooi and Shumin Deng. 2024. Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.acl-long.782

@inproceedings{zhang2024exploring,
  title = {{Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View}},
  author = {Jintian Zhang, Xin Xu, Ningyu Zhang, Ruibo Liu, Bryan Hooi and Shumin Deng},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.782},
  url = {https://doi.org/10.18653/v1/2024.acl-long.782}
}

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