Yes doing an SAT test with the answer key isn’t intelligent because that’s in your “database” and is just a matter of copying over the answers. LLMs don’t do this though, it doesn’t do a lookup of past SAT questions it’s seen and answer it, it uses some process of “reasoning” to do it.
You’ve now reduced the “process of reasoning” to hitting the autocomplete button until your keyboard spits out an answer from a database of prior conversations. It might be cleverly designed, but generative models are no more intelligent than an answer key or a library’s card catalog. Any “intelligence” they appear to encode actually comes from the people who did the work to assemble the training database.
Not_mikey@lemmy.world 11 months ago
This is not how LLMs work, they are not a database nor do they have access to one. They are a trained neural net with a set of weights on matrices that we don’t fully understand. We do know that it can’t possibly have all the information from its training set since the training sets (measured in tb or pb) are orders of magnitude bigger than the models (measured in gb). The llm itself is just what it learned from reading all the training data, just like how you don’t memorize every passage in a book you read, just core concepts, relationships and lessons. So if I ask you " who was gatsbys love interest?" You don’t remember the line and page of the text that says he loves Daisy, your brain just has a strong connection of neurons between Gatsby, Daisy , love, longing etc. that produces the response “Daisy”. The same is true in an LLM, it doesn’t have the whole of the great Gatsby in its model but it too would have a strong connection somewhere between Gatsby, Daisy, love etc. to answer the question.
What your thinking of are older chatbots like Siri or Google assistant which do have a set of preset responses mixed in with some information from a structured database.
knightly@pawb.social 11 months ago
Please do explain how you think they make LLMs without a database of training examples to build a statistical model from.
I.e. “a model that encodes a database”.
I.e., “we applied a very lossy compression algorithm to this database”.
Check out the demoscene sometime, you’ll be surprised how much complexity can be generated from a very small set of instructions. I’ve seen entire first person shooter video games less than 100kb in size that algorithmically generate gigabytes of texture data at runtime. The idea that a mere 1,000x non-lossless compression of text would be impossible is laughable, especially when lossless text compression using neural network techniques achieved a 250x compression ratio years ago.
Not_mikey@lemmy.world 11 months ago
If LLMs were just lossy encodings of their database they wouldn’t be able to answer any questions outside of there training set. They can though, and quite well as shown by the fact you can give it completely made up information that it can’t possibly have “seen” and it will go along with it and give plausible answers. That is where it’s intelligence lyes and what separates it from older chatbots like Siri that cannot infer and are bound by the database they pull from.
How do you explain the hallucinations if the llm is just a complex lookup engine? You can’t lookup something you’ve never seen.
knightly@pawb.social 11 months ago
Of course they could, in the same way that hitting the autocomplete key can finish a half-completed sentence you’ve never written before.
The fact that models can produce useful outputs from novel inputs is the whole reason why we build models. Your argument is functionally equivalent to the claim that wind tunnels are intelligent because they can characterise the aerodynamics of both old and new kinds of planes.
For the same reason that a random number generator is capable of producing never-before-seen strings of digits. LLM inference engines have a property called “temperature” that governs how much randomness is injected into their responses:
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