Large language models like GPT-4 have achieved remarkable proficiency in a broad spectrum of language-based tasks, some of which are traditionally associated with hallmarks of human intelligence. This has prompted ongoing disagreements about the extent to which we can meaningfully ascribe any kind of linguistic or cognitive competence to language models. Such questions have deep philosophical roots, echoing longstanding debates about the status of artificial neural networks as cognitive models. This article -- the first part of two companion papers -- serves both as a primer on language models for philosophers, and as an opinionated survey of their significance in relation to classic debates in the philosophy cognitive science, artificial intelligence, and linguistics. We cover topics such as compositionality, language acquisition, semantic competence, grounding, world models, and the transmission of cultural knowledge. We argue that the success of language models challenges several long-held assumptions about artificial neural networks. However, we also highlight the need for further empirical investigation to better understand their internal mechanisms. This sets the stage for the companion paper (Part II), which turns to novel empirical methods for probing the inner workings of language models, and new philosophical questions prompted by their latest developments.

翻译：诸如GPT-4这样的大型语言模型在广泛的语言任务中展现出显著能力，其中一些任务传统上与人类智能的标志相关联。这引发了关于我们能否有意义地将任何形式的语言或认知能力归属于语言模型的持续分歧。这类问题具有深厚的哲学根源，与关于人工神经网络作为认知模型地位的长期争论相呼应。本文（两篇姊妹论文中的第一篇）既为哲学家提供了语言模型的入门介绍，也对它们与认知科学哲学、人工智能和语言学中经典争论的相关性进行了有观点的综述。我们涵盖的主题包括组合性、语言习得、语义能力、基础化、世界模型以及文化知识的传播。我们认为，语言模型的成功挑战了关于人工神经网络的若干长期假设。然而，我们也强调需要进一步的实证研究以更好地理解其内部机制。这为姊妹论文（第二篇）奠定了基础，后者将转向探测语言模型内部运作的新型实证方法，以及其最新发展引发的新哲学问题。