用大模型挖掘物体部件与材料知识，构建可信赖的常识数据库。
Large Language Models as a Tool for Mining Object Knowledge
- 通过少样本和零样本提示，让大模型识别物体的组成部件与材质
- 成功提取约2300种物体及其子类的部件与材料知识
- 为多跳问答和物体结构推理提供可解释的知识源
常识知识对机器理解世界至关重要。大语言模型（LLMs）在生成类人文本方面表现出色，但因其回答依据不透明且常虚构冷门实体或技术领域的事实，难以作为可信智能系统。本文假设：大模型对日常物体的一般知识总体上是可靠的。基于此，我们研究了其对常见物理物件（如部件与材料）的显式知识提取能力。工作重点区分了构成整体物体的物质与构成其部件的物质——这一在知识库构建中长期被忽视的差异。采用五例上下文提示（few-shot）与零样本多步提示（zero-shot multi-step prompting），我们生成了一个包含约2300个物体及其子类的部件与材料数据仓库。评估表明，大模型在知识覆盖度与准确性方面表现良好。该成果将有助于推动对象结构与组成推理的AI研究，并可作为大模型进行多跳问答的显式知识源（类似知识图谱）。
原文摘要 · Abstract (English)
Commonsense knowledge is essential for machines to reason about the world. Large language models (LLMs) have demonstrated their ability to perform almost human-like text generation. Despite this success, they fall short as trustworthy intelligent systems, due to the opacity of the basis for their answers and a tendency to confabulate facts when questioned about obscure entities or technical domains. We hypothesize, however, that their general knowledge about objects in the everyday world is largely sound. Based on that hypothesis, this paper investigates LLMs' ability to formulate explicit knowledge about common physical artifacts, focusing on their parts and materials. Our work distinguishes between the substances that comprise an entire object and those that constitute its parts$\unicode{x2014}$a previously underexplored distinction in knowledge base construction. Using few-shot with five in-context examples and zero-shot multi-step prompting, we produce a repository of data on the parts and materials of about 2,300 objects and their subtypes. Our evaluation demonstrates LLMs' coverage and soundness in extracting knowledge. This contribution to knowledge mining should prove useful to AI research on reasoning about object structure and composition and serve as an explicit knowledge source (analogous to knowledge graphs) for LLMs performing multi-hop question answering.
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