Chain of thought (CoT) is a reasoning framework that can enhance the performance of Large Language Models (LLMs) on complex inference tasks. In particular, among various studies related to CoT, multi-path inference stands out as a simple yet effective improvement. However, there is no optimal setting for the number of inference paths. Therefore, we have to increase the number of inference paths to obtain better results, which in turn increases the inference cost. To address this limitation, we can utilize question-related role templates to guide LLMs into relevant roles, thereby increasing the possibility of correct inferences for each path and further reducing dependence on the number of inference paths while improving reasoning accuracy. However, placing LLMs into specific roles may reduce their reasoning diversity and performance on a few tasks where role dependence is low. To alleviate the excessive immersion of the LLM into a specific role, we propose Nash CoT by constructing a competitive system on each path that balances the generation from role-specific LLMs' and the general LLMs' generation, thereby ensuring both effective role adoption and diversity in LLM generation further maintaining the performance of multi-path inference while reducing the requirement of the number of inference paths. We evaluate Nash CoT across various inference tasks, including Arabic Reasoning, Commonsense Question Answering, and Symbolic Inference, achieving results that are comparable to or better than those of multi-path CoT with the equal number of inference paths.

翻译：思维链（CoT）是一种能够提升大语言模型（LLM）在复杂推理任务上性能的推理框架。特别是在与CoT相关的众多研究中，多路径推理因其简洁有效的改进而备受关注。然而，推理路径的数量并无最优设定。因此，我们不得不增加推理路径数以获得更好的结果，但这反过来又增加了推理成本。为应对这一局限，我们可以利用与问题相关的角色模板来引导LLM进入相应角色，从而提升每条路径做出正确推理的可能性，在提高推理准确性的同时进一步降低对推理路径数量的依赖。然而，将LLM置于特定角色可能会降低其推理多样性，并在少数角色依赖性较低的任务上影响性能。为缓解LLM过度沉浸于特定角色的问题，我们提出纳什思维链（Nash CoT），通过在每条路径上构建一个竞争系统，平衡来自角色化LLM与通用LLM的生成结果，从而确保LLM既能有效采纳角色又保持生成多样性，进而在降低对推理路径数量要求的同时维持多路径推理的性能。我们在多种推理任务上评估了Nash CoT，包括阿拉伯推理、常识问答和符号推理，其结果在使用同等数量推理路径的情况下，达到或超越了多路径CoT的性能。