Thursday, 23 January 2025

Are we under appreciating LLMs?

Something been mulling for a bit. I wonder if we don't under appreciate LLMs partly because we want to give too much credit to humans. While humans are more than language (take that Structuralists) the language part of them is actually not that special. When we see animals that do not have such a powerful mastery of symbols we should not dismiss them so easily perhaps. Wire a ChatGPT into a monkey or rabbit brain and I reckon you would have a very human like adversary.

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Is the theory behind LLMs similar to Strawson's model of language>
ChatGPT said:
ChatGPT

Strawson’s model of language and the theoretical foundations of large language models (LLMs) like GPT have some conceptual overlaps, but they are fundamentally different in their goals, methods, and philosophical assumptions.

Strawson’s Model of Language

P.F. Strawson, a philosopher of language, is known for his work on ordinary language philosophy and the theory of meaning. His approach emphasizes:

  1. Pragmatic Context: Language is embedded in human practices and relies on context, intention, and the shared understanding of its users.
  2. Speech Acts and Reference: Strawson argued that meaning is not just a property of sentences but emerges through acts of reference and communication.
  3. Philosophical Intent: His focus was on understanding how language operates in practical, everyday communication, often critiquing formal systems like those developed by Bertrand Russell.

In Strawson’s view, the meaning of words or sentences is inseparable from the context in which they are used and the intentions of the speaker.


Large Language Models (LLMs)

LLMs, like GPT, are based on statistical and computational models of language, which include:

  1. Probabilistic Patterns: LLMs learn from vast datasets of text to predict the likelihood of word sequences.
  2. Context Representation: They use mechanisms like attention (in transformers) to capture contextual dependencies in text.
  3. No Explicit Intentions or Reference: LLMs do not have intrinsic understanding, intentions, or the ability to make references in the philosophical sense. Their "understanding" is derived from patterns in data, not from engaging in a shared human context.

While LLMs can mimic pragmatic language use (e.g., responding appropriately to context), they achieve this by leveraging statistical correlations, not by grasping human intentions or engaging in genuine communicative acts.


Key Differences and Overlaps

AspectStrawson's ModelLLMs
Nature of MeaningContextual, pragmatic, tied to intentionEmergent from statistical patterns in data
GoalPhilosophical analysis of language usePredictive text generation and processing
Context UseDeeply tied to speaker-hearer dynamicsEncoded as computational embeddings
UnderstandingInvolves intentionality and referential actsSimulated through learned associations

Possible Conceptual Overlap

  • Both Strawson’s model and LLMs highlight the importance of context in language.
  • LLMs’ probabilistic approach to language reflects a sort of functional imitation of the pragmatics Strawson emphasizes, though without the philosophical depth of intentionality or meaning.

In summary, while Strawson’s work seeks to uncover the philosophical essence of meaning, LLMs provide a computational mechanism for approximating linguistic output. The two are different in purpose, with LLMs being tools for processing and generating text and Strawson’s model offering a framework for understanding the nature of linguistic meaning in human life.

You said:
Doesn't Strawson envisage language to be a huge network with the meaning of a word dependent on the rest of the network. Similarly LLMs have a vector space where words have a relationship to all other words
ChatGPT said:
ChatGPT

You're absolutely right to point out this resemblance, and it's a compelling analogy. Both Strawson's philosophical perspective on language and the structure of large language models (LLMs) share a notion of interconnectedness, where the meaning of a word depends on its relationship to a broader network. Let's explore this in more depth.


Strawson's Network of Language

  1. Relational Meaning: Strawson, like other ordinary language philosophers, viewed language as fundamentally relational. The meaning of a word isn't isolated or intrinsic but arises from:

    • Its connections to other words.
    • Its use within sentences and broader linguistic practices.

    In this sense, Strawson saw language as a dynamic and holistic system, where the meaning of any one element depends on its place in the broader web of communication.

  2. Holism and Context: This approach is in line with linguistic holism—the idea that words and phrases derive their meaning from the interplay within the entire linguistic system, much like a piece in a jigsaw puzzle.


LLMs and Vector Spaces

  1. Word Embeddings and Relational Meaning: LLMs, like GPT, use embeddings to represent words or tokens as vectors in a high-dimensional space. In this space:

    • The meaning of a word is defined by its relationships to other words, encoded as distances and directions in the vector space.
    • Similar or related words are clustered together, reflecting patterns in how they co-occur in the training data.

    For example, in an LLM, "king" and "queen" might have similar vectors, with their difference corresponding to a gender relationship, illustrating how relational meaning is embedded.

  2. Holism in LLMs: Just as Strawson emphasized that the meaning of a word depends on its linguistic context, LLMs operationalize this idea using context-sensitive representations. Attention mechanisms in transformers ensure that:

    • The model dynamically adjusts the representation of a word based on the other words in a given context.
    • This creates a network-like structure of meaning, where every word's contribution to meaning is context-dependent.

Key Parallels Between Strawson and LLMs

AspectStrawson’s ModelLLMs
Meaning as RelationalWords derive meaning from their role in a language network.Words are represented as vectors, defined by relationships to others.
Context DependenceMeaning depends on use within specific linguistic contexts.Contextual embeddings capture meaning dynamically within sentences.
Holistic NatureLanguage is a network; parts gain meaning through the whole.Language is modeled as interdependent word representations in a vector space.

Differences in Depth

While the parallels are striking, there are some key differences:

  1. Philosophical Depth:

    • For Strawson, the network is grounded in human intention, social practices, and communication, making meaning inherently tied to human experience.
    • For LLMs, the network is a mathematical abstraction, detached from the intentionality or lived experience that Strawson emphasized.
  2. Static vs. Dynamic:

    • Strawson's view of meaning evolves through actual use in human interaction, adapting and changing.
    • LLMs operate on a static dataset (at least during training), and while they generate context-sensitive outputs, they don't "adapt" meanings through real interaction in the philosophical sense.

Conclusion

The analogy between Strawson's network of language and LLMs' vector spaces is illuminating. Both frameworks highlight that meaning is not isolated but depends on relationships within a larger system. However, while LLMs mathematically approximate this interconnectedness, they lack the depth of human intentionality and the pragmatic grounding in life that Strawson emphasized. Still, their structural similarity shows how computational models can mirror philosophical insights into language.

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