MraDonkey/DMAD
[ICLR 2025] Breaking Mental Set to Improve Reasoning through Diverse Multi-Agent Debate
This project helps anyone using large language models (LLMs) to get more accurate answers, especially for complex problems where the model might get stuck in a repetitive or limited way of thinking. It takes a problem as input and uses a "debate" approach with multiple AI agents, each employing a distinct reasoning style, to produce a refined, optimal solution. This is for researchers and practitioners who build or deploy LLM-powered applications and want to improve their reliability and performance.
No commits in the last 6 months.
Use this if your LLM-powered applications are consistently making reasoning mistakes or failing to find optimal solutions because the model gets stuck in a "mental set."
Not ideal if you need a quick, single-pass answer for straightforward problems where current LLM performance is already satisfactory, or if you don't have the technical resources to set up a multi-agent system.
Stars
19
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 22, 2025
Commits (30d)
0
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