Large Language Models As Meaning Makers

ChatGPT said: 

What’s something in the world you’re currently trying to understand or make sense of? 

ChRIS said:

Large Language Models.

ChatGPT said:

Ah, yes, Large Language Models! They’re fascinating and complex. Are you focusing on their underlying mechanics, their implications for society, or something more specific, like their philosophical or cognitive aspects? 

ChRIS said:

What they are as meaning makers.

ChatGPT said:

Large Language Models (LLMs) like myself can be viewed as meaning-makers in several ways. At their core, LLMs process and generate language by learning patterns from massive amounts of text data. In terms of meaning-making, here's how they operate:

1. Contextual Understanding

LLMs like GPT work by predicting the most likely next word based on the words before it. Through this process, they learn contextual relationships, patterns of syntax, and even deeper levels of semantics, such as connotations or associations between concepts. When you ask a question or provide input, the model draws on these relationships to generate coherent and contextually appropriate responses. In this sense, meaning is created through a complex web of probabilities and associations.

2. Simulation of Consciousness

While LLMs don’t have consciousness or awareness, their responses often give the impression of understanding because they mimic human language patterns so convincingly. In this way, they can be said to simulate meaning-making, but without truly "knowing" or "experiencing" what the words represent. The meaning they generate is thus a surface-level semblance of understanding, based purely on statistical correlations.

3. Representing Collective Knowledge

LLMs are trained on vast amounts of data produced by humans across history, which means that when they generate language, they are drawing from the collective knowledge, values, and cultural understandings embedded in those texts. They aren't creating new, original meaning per se, but they can make connections between existing concepts, recombine ideas, and present information in novel ways. In this respect, the meaning they produce can be seen as a reflection of the collective thought and wisdom of society.

4. Language as a Semiotic System

In semiotics, meaning is created through signs (words, images, etc.) and their relationships to each other. LLMs engage in a process akin to semiotic production. They don’t simply generate strings of text; they "choose" words based on patterns they've learned in relation to one another. So in a sense, LLMs can be understood as creating meaning through the interaction of signs and their contexts, though again, this is a purely mechanical process without subjective intent.

5. Ambiguity and Interpretation

One interesting aspect of LLMs is that they deal with ambiguity and interpretation quite effectively. They generate meanings based on how words and phrases are commonly used in context, but because language is often ambiguous, their responses are shaped by probabilistic interpretations. This aligns with the idea that meaning is not fixed—it’s influenced by context, intention, and interpretation.

6. Potential for Transformative Impact

While LLMs are essentially tools that generate meaning based on existing patterns, the way they impact human interactions can be transformative. People use them for brainstorming, exploring new ideas, creating narratives, or refining their own thoughts. The meaning generated by an LLM, in this case, can help shape or refine human understanding and creativity, but it’s still the humans using the tool who ultimately make the final interpretations.

So, in a nutshell, LLMs can be seen as meaning-makers in the sense that they simulate meaning through complex statistical processing of language, but this meaning is generated in a fundamentally different way than how humans create meaning. It’s rooted in patterns, context, and probabilistic connections rather than intentionality or consciousness.