LLMs produce language. And you're right, if we restricted claims to that, no one would object. It might even be scientifically accurate, shock of shocks.
If you claim LLMs produce thought, it's equivalent to claiming light bulbs undergo fission. Pure wish fulfillment. Language is not the extent of thought, and calling a language producing machine necessarily a thinking machine is an old old mistake.
> If you claim LLMs produce thought, it's equivalent to claiming light bulbs undergo fission
You're making a huge logical leap. There is nothing equivalent about these claims other than that they are made in English.
> calling a language producing machine necessarily a thinking machine is an old old mistake.
Nobody claims that all language models think. The small markov chain language models of old clearly aren't thinking and produce a lot of gibberish. The difference is that, to the surprise of many people several years ago, but to the surprise of nobody who has been following along today, the corpus of all text produced by humans contains within it information about how the world works and also information about how to reason. Using that corpus to train a sufficiently large language model causes the language model to learn a world model and a reasoning model in order to produce text that matches the training data. The reasoning model can be used to perform longer chain thinking with test time compute techniques. People who think deeply for a living recognize thinking when they see it. https://scottaaronson.blog/?p=9979
>You're making a huge logical leap. There is nothing equivalent about these claims other than that they are made in English.
You’re the one making the logical leap claiming there’s thinking.
And yes, language contains a model of the world. And in so far as that’s compressed in a model, it has a world model. But its world model is static. Its weights don’t change when it gains experience, after its training period. To say that it’s “thinking” is bizarre when it remains untouched entirely by the process.
> You’re the one making the logical leap claiming there’s thinking.
How? What problem that previously required human thinking is not solvable by these systems? You're applying some magical definition to thinking that you have not made public.
> Its weights don’t change when it gains experience, after its training period. To say that it’s “thinking” is bizarre when it remains untouched entirely by the process.
Two mistakes:
1. You don't need to learn to think.
2. Even the most basic sequence models update their internal state as you feed in tokens. You don't need to update the model weights themselves to learn.
If you claim LLMs produce thought, it's equivalent to claiming light bulbs undergo fission. Pure wish fulfillment. Language is not the extent of thought, and calling a language producing machine necessarily a thinking machine is an old old mistake.