Large Language Models (LLMs) with chain-of-thought (COT) prompting have demonstrated impressive abilities on simple nature language inference tasks. However, they tend to perform poorly on Multi-hop Question Answering (MHQA) tasks due to several challenges, including hallucination, error propagation and limited context length. We propose a prompting method, Finite State Machine (FSM) to enhance the reasoning capabilities of LLM for complex tasks in addition to improved effectiveness and trustworthiness. Different from COT methods, FSM addresses MHQA by iteratively decomposing a question into multi-turn sub-questions, and self-correcting in time, improving the accuracy of answers in each step. Specifically, FSM addresses one sub-question at a time and decides on the next step based on its current result and state, in an automaton-like format. Experiments on benchmarks show the effectiveness of our method. Although our method performs on par with the baseline on relatively simpler datasets, it excels on challenging datasets like Musique. Moreover, this approach mitigates the hallucination phenomenon, wherein the correct final answer can be recovered despite errors in intermediate reasoning. Furthermore, our method improves LLMs' ability to follow specified output format requirements, significantly reducing the difficulty of answer interpretation and the need for reformatting.

基于有限状态机 (FSM) 的提示方法增强了大型语言模型 (LLM) 在复杂任务中的推理能力，改善了多跳问答任务中的幻觉、错误传播和上下文长度的限制。通过迭代将问题分解为多轮子问题，并按照自动机的格式根据当前结果和状态决定下一步骤，FSM 逐个处理子问题，提高了每一步答案的准确性。在基准测试中，我们的方法显示了很好的效果。此外，该方法改善了 LLM 遵循指定输出格式要求的能力，显著降低了答案解释的难度和重新格式化的需求。

FSM: 基于有限状态机的多跳问答零样本提示范式