半结构化思维链：整合多源知识以提升语言模型推理能力
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning
AI总结：
本文提出半结构化思维链提示方法，无缝整合参数化记忆、文本文档和知识图谱三类知识，在开放域多跳问答中显著超越现有方法，甚至优于需要微调的技术。
摘要：
在使用大语言模型处理知识密集型任务时，一个重要的开放问题是如何有效整合来自三个来源的知识：模型的参数化记忆、外部结构化知识和外部非结构化知识。现有的大多数提示方法要么仅依赖其中一个或两个来源，要么需要反复调用大语言模型来生成相似或相同的内容。在本工作中，我们通过引入一种新颖的半结构化提示方法来克服这些局限，该方法将模型的参数化记忆与来自文本文档的非结构化知识以及来自知识图谱的结构化知识无缝整合。在开放域多跳问答数据集上的实验结果表明，我们的提示方法显著超越了现有技术，甚至超过了那些需要微调的方法。
英文摘要：
An important open question in the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric memory, external structured knowledge, and external unstructured knowledge. Most existing prompting methods either rely on one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. In this work, we overcome these limitations by introducing a novel semi-structured prompting approach that seamlessly integrates the model's parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. Experimental results on open-domain multi-hop question answering datasets demonstrate that our prompting method significantly surpasses existing techniques, even exceeding those that require fine-tuning.
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。
