将大模型视为符号机器，用语言理论重新理解AI的文本生成机制。
Language Models as Semiotic Machines: Reconceptualizing AI Language Systems through Structuralist and Post-Structuralist Theories of Language
- 用索绪尔符号系统解释词向量的关联性运作。
- 借德里达思想指出模型本质是统计化的书写行为。
- 揭示生成式模型如何体现意义动态流动的后结构主义特征。
本文提出一种新框架，将大语言模型（LLMs）重新定义为符号机器而非人类认知的模仿。基于索绪尔的结构主义与德里达的后结构主义语言理论，论文分三部分展开：首先，阐释word2vec算法在索绪尔语言作为符号关系系统的框架下如何运作；其次，应用德里达对索绪尔的批判，将‘书写’（l'ecriture）确立为LLMs所建模的对象，视机器的‘心智’为符号行为的统计近似；最后，指出现代LLMs通过‘下一个标记生成’机制，有效捕捉了意义不固定的后结构主义特性。该框架提供了一种替代视角，有助于更深入评估LLMs的优势与局限，为未来研究开辟新路径。
原文摘要 · Abstract (English)
This paper proposes a novel framework for understanding large language models (LLMs) by reconceptualizing them as semiotic machines rather than as imitations of human cognition. Drawing from structuralist and post-structuralist theories of language-specifically the works of Ferdinand de Saussure and Jacques Derrida-I argue that LLMs should be understood as models of language itself, aligning with Derrida's concept of 'writing' (l'ecriture). The paper is structured into three parts. First, I lay the theoretical groundwork by explaining how the word2vec embedding algorithm operates within Saussure's framework of language as a relational system of signs. Second, I apply Derrida's critique of Saussure to position 'writing' as the object modeled by LLMs, offering a view of the machine's 'mind' as a statistical approximation of sign behavior. Finally, the third section addresses how modern LLMs reflect post-structuralist notions of unfixed meaning, arguing that the "next token generation" mechanism effectively captures the dynamic nature of meaning. By reconceptualizing LLMs as semiotic machines rather than cognitive models, this framework provides an alternative lens through which to assess the strengths and limitations of LLMs, offering new avenues for future research.
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