语言与思维：来自大语言模型的视角
Language and Thought: The View from LLMs
AI总结：
本文以大语言模型的推理表现为切入点，验证丹尼特关于语言深刻影响思维的论点，指出语言编码的抽象高效性是LLMs跨域推理的核心，并探讨其对理解人类心智中语言作用的启示。
摘要：
丹尼尔·丹尼特（Daniel Dennett）在1996年的《心灵的种类》（*Kinds of Minds*）中推测：“或许当你为一种心灵加入语言后，它与没有语言的心灵差异极大，以至于将二者都称为心灵是一种错误。”近期的人工智能研究可被视为对丹尼特这一论点的检验，其探索了接受过语言训练与未接受语言训练的人工智能系统的表现。本文认为，大语言模型（Large Language Models，LLMs）在推理能力上取得的成功（尽管仍有局限），支撑了丹尼特关于语言对思维影响的激进观点。本文提出，语言编码的抽象性与高效性，是LLMs能够在广泛领域内执行推理的核心原因。用一句口号概括：语言让推理在计算上变得可行。本文还评估了人工智能领域的这些结果，对理解语言在人类生物心智运作中的作用有何启示。
英文摘要：
Daniel Dennett speculated in *Kinds of Minds* 1996: "Perhaps the kind of mind you get when you add language to it is so different from the kind of mind you can have without language that calling them both minds is a mistake." Recent work in AI can be seen as testing Dennett's thesis by exploring the performance of AI systems with and without linguistic training. I argue that the success of Large Language Models at inferential reasoning, limited though it may be, supports Dennett's radical view about the effect of language on thought. I suggest it is the abstractness and efficiency of linguistic encoding that lies behind the capacity of LLMs to perform inferences across a wide range of domains. In a slogan, language makes inference computationally tractable. I assess what these results in AI indicate about the role of language in the workings of our own biological minds.
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。
