The ubiquity of audio commutation technologies, particularly telephone, radio, and TV, have had a significant affect on language. They further spread English around the world making it more accessible and more necessary for lower social and economic classes, they led to the blending of dialects and the death of some smaller regional dialects. They enabled the rapid adoption of new words and concepts.
How will LLMs affect language? Will they further cement English as the world’s dominant language or lead to the adoption of a new lingua franca? Will they be able to adapt to differences in dialects or will they force us to further consolidate how we speak? What about programming languages? Will the model best able to generate usable code determine what language or languages will be used in the future? Thoughts and beliefs generally follow language, at least on the social scale, how will LLM’s affects on language affect how we think and act? What we believe?
Do you actually believe this?
Yes. I’m also very happy to be proven wrong in the years to come.
If you ask an LLM a question, and it gives you a response that indicates it has understood your question correctly, and you are able to understand its response that far, then the LLM has done it’s job, regardless of if the answer is correct
I don’t want to get too philosophical here, but you cannot detach understanding / comprehension from the accuracy of the reply, given how LLMs work.
An LLM, through its training data, establishes what an answer looks like based on similarity to what it’s been taught.
I’m simplifying here, but it’s like an actor in a medical drama. The actor is given a script that they repeat, that doesn’t mean they are a doctor. After a while the actor may be able to point out an inconsistency in the script because they remember that last time a character had X they needed Y. That doesn’t mean they are right, or wrong, nor does it make them a doctor, but they sound like they are.
This is the fundamental problem with LLMs. They don’t understand, and in generating replies they just repeat. It’s a step forward on what came before, that’s definitely true, but repetition is a dead end because it doesn’t hold up to application or interrogation.
The human-machine interface part, of being able to process natural language requests and then handing off those requests to other systems, operating in different ways, is the most likely evolution of LLMs. But generating the output themselves is where it will fail.
So I feel like we agree here. LLMs are a step to solving a low level human problem, i just don’t see that as a dead end… If we don’t take the steps, we’re still in the oceans. We’re also learning a lot in the process ourselves, and that experience will carry on.
I appreciate your analogy, I am well aware LLMs are just clever recursive conditional queries with big semi self-updating datasets.
Regardless of whether or not something replaces LLMs in the future, the data and processing that’s gone into that data, will likely be used along with the lessons were learning now. I think they’re a solid investment from any angle.
Regardless of whether or not something replaces LLMs in the future, the data and processing that’s gone into that data, will likely be used along with the lessons were learning now. I think they’re a solid investment from any angle.
I’m a big proponent of research for the sake of research, so I agree that lessons will be learnt.
But to go back to Ops original question, how will LLMs affect spoken language, they won’t.
But to go back to Ops original question, how will LLMs affect spoken language, they won’t.
That’s a rather closed minded conclusion. It makes it sound like you don’t think they have the chance.
LLMs have the potential to pave the way to aligning spoken language, perhaps even evolving human communication to a point where speech is an occasional thing because it’s really inefficient.
