Problem-Solving in Language Model Networks

• URL: http://arxiv.org/abs/2406.12374v3
• Date: Tue, 9 Jul 2024 13:05:58 GMT
• Title: Problem-Solving in Language Model Networks
• Authors: Ciaran Regan, Alexandre Gournail, Mizuki Oka,
• Abstract summary: This work extends the concept of multi-agent debate to more general network topologies. It measures the question-answering accuracy, influence, consensus, and the effects of bias on the collective.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To improve the reasoning and question-answering capabilities of Large Language Models (LLMs), several multi-agent approaches have been introduced. While these methods enhance performance, the application of collective intelligence-based approaches to complex network structures and the dynamics of agent interactions remain underexplored. This work extends the concept of multi-agent debate to more general network topologies, measuring the question-answering accuracy, influence, consensus, and the effects of bias on the collective. The results show that random networks perform similarly to fully connected networks despite using significantly fewer tokens. Furthermore, a strong consensus among agents correlates with correct answers, whereas divided responses typically indicate incorrect answers. Analysing the influence of the agents reveals a balance between self-reflection and interconnectedness; self-reflection aids when local interactions are incorrect, and local interactions aid when the agent itself is incorrect. Additionally, bias plays a strong role in system performance with correctly biased hub nodes boosting performance. These insights suggest that using random networks or scale-free networks with knowledgeable agents placed in central positions can enhance the overall question-answering performance of multi-agent systems.

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