Structuralism and Large Language Models
In this article, I will compare two seemingly unrelated concepts: Structuralism and Large Language Models. Despite their apparent differences, I aim to uncover the underlying similarities that connect these two domains.
Structuralism, a theoretical framework that emerged in the mid-20th century, examines the underlying structures and patterns that form language. Examining the relationships between elements within language systems to uncover implicit patterns and rules. Ferdinand de Saussure, one of the most prominent figures in Structuralism, posited that the meaning of a word derives from its closeness or distance from other words, rather than any resemblance to the real world object it signifies.
Large Language Models (LLMs) are a recent advancement in AI that use deep learning techniques to understand and generate responses based on vast amounts of training data. These models encode words in high-dimensional vectors. Words with similar meanings or usage patterns tend to have vectors that are close together in the vector space, while words with different meanings are further apart. LLMs excel at generalising from the structures they’ve learned during training. When presented with input text they haven’t seen before, they use their learned associations to make predictions about the next word. LLMs do not have an understanding of the real world objects they reference, they lack comprehension or awareness of the meaning behind the words and operate purely on statistical associations and patterns learned from the data.
While Structuralism and LLMs differ in methodology and theoretical underpinnings, they share common principles and objectives. Both approaches aim to uncover the structures and patterns within language.
Emphasis on Structure: Both Structuralism and LLMs prioritise the understanding of fundamental structures. In Structuralism, by uncovering these patterns, structuralists aim to reveal a deeper framework and principle that can be applied to language. LLMs gain a deep understanding of the structure of language from the training data. They learn the syntactic rules governing the arrangement of words in sentences, including grammar, word order, and sentence structure.
Focus on Relationships: Structuralism highlights the importance of relationships between elements within a system rather than isolated entities. Similarly, LLMs analyse relationships between words, phrases, and contexts to generate meaningful and coherent language output.
Analysis of Systems: Structuralism seeks to understand systems of meaning and communication within language, and the greater context of culture and society. Similarly, LLMs operate within complex systems of linguistic data, analysing patterns and relationships to generate text that aligns with human language conventions.
Deconstruction and Reconstruction: Structuralism involves the deconstruction of texts or cultural artefacts to reveal underlying structures and meanings. Similarly, LLMs deconstruct input text into patterns and relationships, and then reconstruct it to generate output that resembles expected human responses.
Binary Oppositions: Meaning is generated through the contrast or opposition between two opposing terms. These terms define each other by their differences. For example, light is defined by its opposition to darkness, and good is defined by its opposition to evil. Similarly, LLMs, while they don’t explicitly use binary oppositions, may implicitly capture some aspects of binary oppositions in their training data. For example, they may learn associations between contrasting concepts through exposure to texts where these concepts are frequently juxtaposed or contrasted.
In the intersection of Structuralism and LLMs lies a rich opportunity for interdisciplinary inquiry and synthesis. By drawing on the insights of Structuralism, computational linguists can gain a deeper understanding of the formal properties and underlying structures of language. Conversely, Structuralists can leverage computational tools and methodologies to analyse large-scale linguistic data and explore emergent patterns and trends.
As linguistic theory continues to evolve in tandem with advances in AI, bridging the theoretical foundations of Structuralism with the computational realities of LLMs offers a promising avenue for advancing our understanding of language and communication. By combining theoretical rigour with computational prowess, researchers can unlock new insights into the complexities of human language and pave the way for more sophisticated language technologies in the future.
