SR-FoT：面向大型语言模型处理基于知识的推理任务的三段论推理思维框架
SR-FoT: A Syllogistic-Reasoning Framework of Thought for Large Language Models Tackling Knowledge-based Reasoning Tasks
AI总结：
提出SR-FoT框架，通过多阶段三段论演绎推理增强LLMs处理基于知识的复杂推理任务的能力，实验验证其有效性。
摘要：
演绎推理是一种关键的逻辑能力，有助于我们基于已有知识解决复杂问题。尽管通过思维链提示进行了增强，大型语言模型（LLMs）可能仍不会遵循正确的推理路径。提升LLMs的演绎推理能力，并利用其广泛的内置知识来处理各种推理任务，仍然是一个开放性问题。为了模拟人类演绎推理范式，我们提出了一种多阶段的三段论推理思维框架（SR-FoT），该框架使LLMs能够执行三段论演绎推理，以处理复杂的基于知识的推理任务。我们的SR-FoT首先解释问题，然后利用解释和原始问题来提出合适的大前提。接着，它分两个阶段生成并回答小前提问题，以匹配小前提。最后，它引导LLMs使用先前生成的大前提和小前提进行三段论演绎推理，以得出原始问题的答案。在基于知识的推理任务上进行的大量且彻底的实验证明了我们SR-FoT的有效性和优势。
英文摘要：
Deductive reasoning is a crucial logical capability that assists us in solving complex problems based on existing knowledge. Although augmented by Chain-of-Thought prompts, Large Language Models (LLMs) might not follow the correct reasoning paths. Enhancing the deductive reasoning abilities of LLMs, and leveraging their extensive built-in knowledge for various reasoning tasks, remains an open question. Attempting to mimic the human deductive reasoning paradigm, we propose a multi-stage Syllogistic-Reasoning Framework of Thought (SR-FoT) that enables LLMs to perform syllogistic deductive reasoning to handle complex knowledge-based reasoning tasks. Our SR-FoT begins by interpreting the question and then uses the interpretation and the original question to propose a suitable major premise. It proceeds by generating and answering minor premise questions in two stages to match the minor premises. Finally, it guides LLMs to use the previously generated major and minor premises to perform syllogistic deductive reasoning to derive the answer to the original question. Extensive and thorough experiments on knowledge-based reasoning tasks have demonstrated the effectiveness and advantages of our SR-FoT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
