The Advantages of Sparse Communication Topology in Multi-Agent Systems
Addressing Computational Inefficiencies
A significant challenge in large language models (LLMs) is the high computational cost associated with multi-agent debates (MAD).
The fully connected communication topology in multi-agent debates leads to expanded input contexts and increased computational demands.
Current methods involve techniques such as Chain-of-Thought (CoT) prompting and self-consistency, which suffer from limitations and require extensive computational resources.
Introducing a Novel Approach
Google DeepMind researchers introduce a novel approach using sparse communication topology in multi-agent debates to significantly reduce computational costs while maintaining or improving performance.
The approach involves systematic investigation and implementation of neighbor-connected communication strategies, where agents communicate with a limited set of peers rather than all agents.
Experimental Results
The experimental setup includes performance metrics like accuracy and cost savings, and the approach achieved notable improvements in both performance and computational efficiency.
On the MATH dataset, a neighbor-connected topology improved accuracy by 2% over fully connected MAD while reducing the average input token cost by over 40%.
For alignment labeling tasks, sparse MAD configurations showed improvements in helpfulness and harmlessness metrics by 0.5% and 1.0%, respectively, while halving the computational costs.
Advancing the Practical Applicability of Multi-Agent Systems
This research presents a significant advancement in the field of AI by introducing sparse communication topology in multi-agent debates, offering a scalable and resource-efficient solution.
The experimental results highlight the potential impact of this innovation on AI research, showcasing its ability to enhance performance while reducing costs, thereby advancing the practical applicability of multi-agent systems.
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