Large language models show human-like performance in knowledge extraction,
reasoning and dialogue, but it remains controversial whether this performance
is best explained by memorization and pattern matching, or whether it reflects
human-like inferential semantics and world knowledge. Knowledge bases such as
WikiData provide large-scale, high-quality representations of inferential
semantics and world knowledge. We show that large language models learn to
organize concepts in ways that are strikingly similar to how concepts are
organized in such knowledge bases. Knowledge bases model collective,
institutional knowledge, and large language models seem to induce such
knowledge from raw text. We show that bigger and better models exhibit more
human-like concept organization, across four families of language models and
three knowledge graph embeddings.

大型语言模型在知识提取、推理和对话方面显示出与人类相似的表现，但是它们的表现究竟是通过记忆和模式匹配来解释的，还是反映了人类般的推理语义和世界知识，存在争议。本文展示了大型语言模型学习以类似于知识库的方式组织概念，这些知识库提供了推理语义和世界知识的大规模高质量表征。大型语言模型似乎从原始文本中引出这种知识，而更大更好的模型表现出更符合人类的概念组织，涵盖了四个系列的语言模型和三个知识图谱嵌入。

大型语言模型向人类类似的概念组织收敛

Large language models converge toward human-like concept organization

Some defenders of so-called `artificial intelligence' believe that machines
can understand language. In particular, S{\o}gaard has argued in this journal
for a thesis of this sort, on the basis of the idea (1) that where there is
semantics there is also understanding and (2) that machines are not only
capable of what he calls `inferential semantics', but even that they can (with
the help of inputs from sensors) `learn' referential semantics
\parencite{sogaard:2022}. We show that he goes wrong because he pays
insufficient attention to the difference between language as used by humans and
the sequences of inert of symbols which arise when language is stored on hard
drives or in books in libraries.

一些所谓 `人工智能 ' 的辩护者认为机器可以理解语言。特别是，Søgaard 在这篇论文中提出了这样的论点，即（1）哪里有语义，就会有理解，（2）机器不仅能够进行所谓的` 推理语义 '，甚至可以（通过传感器的输入帮助）` 学习 ' 指称语义。然而，我们证明他的观点是错误的，因为他未充分重视语言在人类和硬盘或书籍中存储时出现的差异。