Large Language Models (LLMs) like ChatGPT reflect profound changes in the field of Artificial Intelligence, achieving a linguistic fluency that is impressively, even shockingly, human-like. The extent of their current and potential capabilities is an active area of investigation by no means limited to scientific researchers. It is common for people to frame the training data for LLMs as "text" or even "language". We examine the details of this framing using ideas from several areas, including linguistics, embodied cognition, cognitive science, mathematics, and history. We propose that considering what it is like to be an LLM like ChatGPT, as Nagel might have put it, can help us gain insight into its capabilities in general, and in particular, that its exposure to linguistic training data can be productively reframed as exposure to the diegetic information encoded in language, and its deficits can be reframed as ignorance of extradiegetic information, including supradiegetic linguistic information. Supradiegetic linguistic information consists of those arbitrary aspects of the physical form of language that are not derivable from the one-dimensional relations of context -- frequency, adjacency, proximity, co-occurrence -- that LLMs like ChatGPT have access to. Roughly speaking, the diegetic portion of a word can be thought of as its function, its meaning, as the information in a theoretical vector in a word embedding, while the supradiegetic portion of the word can be thought of as its form, like the shapes of its letters or the sounds of its syllables. We use these concepts to investigate why LLMs like ChatGPT have trouble handling palindromes, the visual characteristics of symbols, translating Sumerian cuneiform, and continuing integer sequences.

翻译：像ChatGPT这样的大型语言模型反映了人工智能领域的深刻变革，实现了令人印象深刻甚至惊人逼真的人类语言流畅度。其当前及潜在能力的范围是一个活跃的研究领域，且远不止限于科学研究者。人们通常将LLM的训练数据视为"文本"甚至"语言"。我们利用来自语言学、具身认知、认知科学、数学和历史等多个领域的思想，审视这一框架的细节。我们提出，正如内格尔可能所言，思考成为像ChatGPT这样的LLM是什么样，有助于我们洞察其总体能力，特别是，其接触语言训练数据的过程可以被富有成效地重新定义为接触语言中编码的叙事信息，而其不足之处则可重新定义为对超叙事信息（包括超叙事语言信息）的无知。超叙事语言信息由语言物理形式中那些任意的方面组成，这些方面无法从LLM（如ChatGPT）所能访问的一维上下文关系（频率、邻接、邻近、共现）中推导得出。粗略地说，一个单词的叙事部分可被视为其功能、意义，即词嵌入中理论向量的信息；而单词的超叙事部分可被视为其形式，如字母的形状或音节的发音。我们运用这些概念来探究为什么像ChatGPT这样的LLM在处理回文、符号的视觉特征、翻译苏美尔楔形文字以及延续整数序列时会遇到困难。