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Large Language Models (LLMs) are evolving at an unprecedented pace and haveexhibited considerable capability in the realm of natural language processing(NLP) with world knowledge. Benefiting from ultra-large-scale training corpora,a single LLM can manage typical NLP tasks competently. However, its performancein executing reasoning tasks is still confined by the limitations of itsinternal representations. To push this boundary further, we introduce Corex inthis paper, a suite of novel general-purpose strategies that transform LLMsinto autonomous agents pioneering multi-model collaborations for complextask-solving. Inspired by human behaviors, Corex is constituted by diversecollaboration paradigms including Debate, Review, and Retrieve modes, whichcollectively work towards enhancing the factuality, faithfulness, andreliability of the reasoning process. These paradigms foster task-agnosticapproaches that enable LLMs to ''think outside the box,'' thereby overcominghallucinations and providing better solutions. Through extensive experimentsacross four different types of reasoning tasks, we demonstrate thatorchestrating multiple LLMs to work in concert yields substantially betterperformance compared to existing methods. Further results and in-depth analysisdemonstrate the cost-effectiveness of our method, facilitating collaborationamong different LLMs and promoting annotation efficiency.

Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration