Summary of Language Models As Semiotic Machines: Reconceptualizing Ai Language Systems Through Structuralist and Post-structuralist Theories Of Language, by Elad Vromen
Language Models as Semiotic Machines: Reconceptualizing AI Language Systems through Structuralist and Post-Structuralist Theories of Language
by Elad Vromen
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for understanding large language models (LLMs) by reconceptualizing them as semiotic machines rather than imitations of human cognition. The authors draw from structuralist and post-structuralist theories, specifically the works of Ferdinand de Saussure and Jacques Derrida, to argue that LLMs should be understood as models of language itself, aligning with Derrida’s concept of ‘writing’ (l’ecriture). The framework is structured into three parts: first, the theoretical groundwork is laid by explaining how the word2vec embedding algorithm operates within Saussure’s framework of language as a relational system of signs; second, Derrida’s critique of Saussure is applied to position ‘writing’ as the object modeled by LLMs, offering a view of the machine’s ‘mind’ as a statistical approximation of sign behavior; and 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can better understand big language models. It’s like trying to figure out what a machine is thinking by looking at how it uses words. The authors use ideas from old philosophers like Saussure and Derrida to show that these machines are really just models of language itself, not copies of human thought. They break this down into three parts: first, they explain how the algorithm for word2vec works within a specific theory of language; second, they apply Derrida’s ideas to show how these machines think about words in a certain way; and finally, they talk about how modern language models reflect what it means to have meaning that changes all the time. Overall, this paper gives us a new way to look at big language models and might help us learn more about them. |
Keywords
» Artificial intelligence » Embedding » Token » Word2vec
