Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration
Paper · 2023 · Qiushi Sun, Zhangyue Yin, Xiang Li et al.
Summary
Even large language models often fail at multi-step reasoning: they make arithmetic slips, misread how variables relate, or write code that does not match the problem. Prompting tricks such as CoT, self-consistency voting and PAL still rely on one model's internal knowledge. Self-correction by a single model has also proven weak. Corex addresses this by treating several LLMs as cooperating agents with assigned personas. It offers three modes:
- Discuss: two teams of agents debate internally, and a judge settles any disagreement.
- Review: agents check and fix each other's reasoning chains or generated programs.
- Retrieve: a model scores and ranks candidate chains to choose the most faithful one.
The authors tested these modes on 18 tasks in four groups: mathematical, symbolic, commonsense and semi-structured reasoning. Corex beat strong baselines, and each mode was strongest on different task types. It also used far fewer tokens than majority-voting methods and needed fewer curated demonstrations. The work suggests that structured teamwork between models is a practical, task-agnostic way to improve LLM reasoning.
Key points
- The three collaboration modes are Discuss (Blue Team vs. Green Team plus a judge, up to T rounds), Review (Review_NL for text chains, Review_Code for programs) and Retrieve (ranking candidate chains).
- Benchmarks include GSM8K, AddSub, SVAMP, SingleEQ, SingleOP, StrategyQA, CSQA, BoolQ, ARC-c, OpenBookQA, Penguin, Repeat Copy and ConvFinQA. Backbones include GPT-3.5-Turbo, GPT-4 and Claude-Instant-1.2.
- On AddSub, Corex matched the accuracy of CoT-SC and ComplexCoT while using only about 5–10% of their compute.
- In Discuss mode, a stronger judge model gives better results, especially on harder tasks. In Retrieve mode, the choice of retriever model matters little.
- Most Discuss sessions converge quickly, but over 10% of ConvFinQA problems needed more than 3 rounds. Code and data: github.com/QiushiSun/Corex.
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 worksComputer Vision and Pattern Recognition · 3,621 works
🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,948 works
Cite
Cite as Qiushi Sun, Zhangyue Yin, Xiang Li, Zhiyong Wu, Xipeng Qiu and Lingpeng Kong. 2023. Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration. arXiv:2310.00280.
@misc{sun2023corex,
title = {{Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration}},
author = {Qiushi Sun, Zhangyue Yin, Xiang Li, Zhiyong Wu, Xipeng Qiu and Lingpeng Kong},
year = {2023},
doi = {10.48550/arxiv.2310.00280},
eprint = {2310.00280},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2310.00280}
}References
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