MoA BEATS GPT4o With Open-Source Models!! (With Code!)

1. MOA, a mixture of agents, outperforms GPT-4.0.

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MOA, leveraging multiple open-source models, surpasses GPT-4.0 in performance, efficiency, and cost-effectiveness, showcasing the power of collaborative AI frameworks.

• MOA achieves a score of 65.1 on Alpaca Eval 2.0, surpassing GPT-4.0 by a substantial margin.
• The collaborative approach of MOA integrates diverse capabilities from various models, resulting in a more robust and versatile combined model.
• While MOA excels in accuracy, it faces challenges with latency, indicating room for future improvements.

2. Leveraging diverse llms enhances model performance.

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Integrating responses from multiple models, even if individually less capable, significantly boosts model accuracy and quality, showcasing the power of collaboration.

• Models benefit from presenting outputs from other models, leading to improved responses.
• Having a variety of proposer outputs enhances the model's performance, emphasizing the value of diverse perspectives.

3. Layered approach in MOA improves response quality.

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Implementing a layered process in MOA, where proposers generate initial responses and aggregators synthesize them, results in progressively enhanced and comprehensive responses.

• Proposers offer nuanced perspectives, while aggregators synthesize responses into high-quality outputs.
• The iterative process through layers refines responses, culminating in robust and comprehensive outputs.