Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
Paper · 2024 · Qineng Wang, Zihao Wang, Ying Su et al.
Summary
Earlier work suggests that LLM agents discussing a problem together reason better than a single model. This paper tests that claim more rigorously. The authors define a general pipeline for designing discussion frameworks with four parts: kick-start prompt, agents, algorithm and result. They also introduce CMD (Conquer-and-Merge Discussion), a new framework modeled on human group discussion. In CMD, agents debate within groups, see only the answers of other groups, and then vote. A secretary agent breaks ties. CMD is compared with Debate, MAD, ReConcile and single-agent baselines on ECQA, GSM8K and FOLIO-wiki, using ChatGPT-3.5, Gemini Pro and Bard as backbones. Prompts are tested both with and without demonstrations. The main finding is that a strong single LLM with a good, demonstration-based prompt performs about as well as the best discussion setups. Discussion clearly helps mainly when no demonstrations are given. This tells practitioners when the extra cost of multi-agent setups is likely to pay off.
Key points
- Setup: Bard runs via PaLM2 text-bison-001. Temperature is fixed at 0.25, discussions last at most 3 rounds, and accuracy is the metric. The multi-LLM CMD uses six agents split into two groups of three, with each group mixing ChatGPT-3.5, Gemini Pro and Bard.
- With demonstrations: average accuracies are close across setups:
- single Gemini Pro: 78.59
- ReConcile: 78.36
- CMD: 78.66
- Without demonstrations: discussion shows a clear gain:
- CMD: 76.93
- ReConcile: 76.11
- best single agent (Gemini Pro): 74.38
- Typical discussion failures: the authors identify two recurring error types, "judge mistake" and "wrong answer propagation."
- Mixed-LLM discussions: agents built on weaker models, such as Bard, improve over the rounds by interacting with stronger ones, such as Gemini Pro.
Institutions
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works
🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,685 works
Cite
Cite as Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong and Yangqiu Song. 2024. Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?. arXiv:2402.18272.
@misc{wang2024rethinking,
title = {{Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?}},
author = {Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong and Yangqiu Song},
year = {2024},
doi = {10.48550/arxiv.2402.18272},
eprint = {2402.18272},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2402.18272}
}References
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