It’s clear that companies are currently unable to make chatbots like ChatGPT comply with EU law, when processing data about individuals. If a system cannot produce accurate and transparent results, it cannot be used to generate data about individuals. The technology has to follow the legal requirements, not the other way around.
This is not correct based on my understanding of LLMs, but I am certainly not an expert. As I understand it, it’s basically a statistics exercise in how they determine what order to put words into. They don’t ‘look stuff up’ in their training data. They probably don’t even have access to their training data once the model is complete. These models are trained on terabytes of data but are small enough to fit in memory, so it’s impossible for them to still have access to all that. But it wouldn’t matter if they did, because that’s not how they work.
LLMs do not look stuff up (except when they have an API that allows them to), but I think OP’s point still stands. The statistical next token predictor metaphor is useful , but in many regards that’s what text and language are. If you can understand that certain words are linked to certain other words, then you should be able to appreciate that certain groups of words can be associated in a way that is functionally the same as data.
I have not memorized the pytorch documentation, but I can use what I understand about pytorch and other libraries to infer specific aspects of the library that I am not familiar with. Functionally, this is no different than if I accessed the documentation directly. If I communicate this information to others I have functioned as a data repository. The repository works on a more abstract and error-prone level, but it works nonetheless.
Here is another very concrete example: LLMs know George Washington’s birthday. Not because they look up that information, but because of the learned associations between George Washington, birthday, and his actual date of birth.
This is what LLM’s can’t do though. They can’t use what they understand because they don’t understand anything. They can’t infer, they can’t reason, they can’t evaluate or compare. They can spit out words that make it look like they did those things, but they didn’t.
Here I think you are behind on the literature. LLMs can infer and reason, and there are whole series of papers that evaluate LLMs for these properties the exact same way we evaluate humans. So if you can’t trust the metrics, then you cannot even assert that humans can reason and infer and understand.
https://arxiv.org/html/2403.04121v1
Good read from a group of computer scientists at Arizona State. Their conclusions are the same as mine but they illustrate the problems better than I ever could.
You linked a paper on planning in LLMs. Planning is largely in the domain of reinforcement learning. The paper you linked conflates reasoning with planning, alongside the obviously biased prose, so the author really doesn’t seem credible. I prefer nuanced and careful evaluations such as: https://www.sciencedirect.com/science/article/pii/S2949719123000298
Without commenting on the content of the paper,
Hm. 🤔
Notice that there are methods, data, and peer reviews that I can freely scrutinize. All things your opinion piece lacks.