While language models are powerful and versatile, they often fail to address highly complex problems. This is because solving complex problems requires deliberate thinking, which has been only minimally guided during training. In this paper, we propose a new method called Cumulative Reasoning (CR), which employs language models in a cumulative and iterative manner to emulate human thought processes. By decomposing tasks into smaller components, CR streamlines the problem-solving process, rendering it both more manageable and effective. For logical inference tasks, CR consistently outperforms existing methods with an improvement up to 9.3%, and achieves the astonishing accuracy of 98.04% on the curated FOLIO wiki dataset. In the context of the Game of 24, CR achieves an accuracy of 94%, which signifies a substantial enhancement of 20% over the previous state-of-the-art method (code is available at https://github.com/iiis-ai/cumulative-reasoning).

翻译：尽管语言模型强大且多功能，但它们往往难以解决高度复杂的问题。这是因为解决复杂问题需要深思熟虑，而这种能力在训练过程中仅得到极小程度的引导。本文提出一种名为累积推理的新方法，该方法以累积和迭代的方式运用语言模型来模拟人类思维过程。通过将任务分解为更小的组成部分，CR简化了问题解决流程，使其既更易于管理又更高效。在逻辑推理任务中，CR持续优于现有方法，提升幅度高达9.3%，并在精选的FOLIO wiki数据集上达到惊人的98.04%准确率。在24点游戏场景中，CR实现了94%的准确率，相比先前最优方法提高了20%（代码可在https://github.com/iiis-ai/cumulative-reasoning获取）。