The framework, implemented with state-of-the-art LLMs like ChatGPT, Bard, and Claude2, has shown significant improvements in reasoning performance. When implemented with GPT-4, it improved the model's initial accuracy by an absolute 10.0%. Experimental results on multiple reasoning datasets have shown that RECONCILE outperforms prior methods and even GPT-4 on some benchmarks. It also achieves faster consensus between agents compared to a multi-agent debate baseline, making it an efficient framework for enhancing LLMs' reasoning capabilities.
Key takeaways:
- RECONCILE is a structured, multi-agent framework designed to enhance the reasoning capabilities of Large Language Models (LLMs) by allowing them to collaboratively solve problems and reach improved consensus through structured discussions.
- Each agent in RECONCILE generates an individual response to a problem, and through a series of structured discussion rounds, they refine their responses based on the insights shared by their peers, striving to reach a consensus.
- RECONCILE has demonstrated significant enhancements in the reasoning performance of state-of-the-art LLMs such as ChatGPT, Bard, and Claude2, and has also improved the initial accuracy of GPT-4 by an absolute 10.0%.
- The framework has shown to improve upon prior methods and outperforms GPT-4 on some benchmarks, and achieves better and faster consensus between agents compared to a multi-agent debate baseline, making it a more efficient framework for enhancing the reasoning capabilities of LLMs.