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CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate

Created by
  • Haebom

Author

Yiliu Sun, Zicheng Zhao, Sheng Wan, Chen Gong

Outline

In this paper, we propose CortexDebate, an improved version of the multi-agent debate (MAD) method to address the problems of hallucination and inference insufficiency of a single large-scale language model (LLM). To address the excessive input context and overconfidence problems of the existing MAD, CortexDebate uses the McKinsey-based Debate Matter (MDM) module, which acts like the white matter of the brain, to build a sparse and dynamically optimized debate graph among LLM agents. MDM integrates the McKinsey trust formula, a trustworthiness measure in sociology, to guide graph optimization through reliable evaluation. We demonstrate the effectiveness of CortexDebate through extensive experiments on eight datasets and four types of tasks.

Takeaways, Limitations

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Takeaways:
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We present an effective multi-agent argumentation (MAD) method to overcome the limitations of single LLM.
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Solving the problems of excessive input context and overconfidence in existing MADs.
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Efficient argument graph construction and optimization via MDM module mimicking white matter of the brain.
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Trustworthy agent evaluation and graph management using the McKinsey Trust Formula.
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Experimental validation of the effectiveness on various datasets and task types.
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Limitations:
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Potential sociological bias due to MDM module's dependence on McKinsey's trust formula.
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Further research is needed on the scalability of the proposed method and its generalization performance to complex tasks.
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The diversity and representativeness of experimental datasets needs to be reviewed.
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