Comment by xanderlewis
1 year ago
I can’t define ‘understanding’ but I can certainly identify a lack of it when I see it. And LLM chatbots absolutely do not show signs of understanding. They do fine at reproducing and remixing things they’ve ‘seen’ millions of times before, but try asking them technical questions that involve logical deduction or an actual ability to do on-the-spot ‘thinking’ about new ideas. They fail miserably. ChatGPT is a smooth-talking swindler.
I suspect those who can’t see this either
(a) are software engineers amazed that a chatbot can write code, despite it having been trained on an unimaginably massive (morally ambiguously procured) dataset that probably already contains something close to the boilerplate you want anyway
(b) don’t have the sufficient level of technical knowledge to ask probing enough questions to betray the weaknesses. That is, anything you might ask is either so open-ended that almost anything coherent will look like a valid answer (this is most questions you could ask, outside of seriously technical fields) or has already been asked countless times before and is explicitly part of the training data.
Your understanding of how LLMs work isn’t at all accurate. There’s a valid debate to be had here, but it requires that both sides have a basic understanding of the subject matter.
How is it not accurate? I haven’t said anything about the internal workings of an LLM — just what it able to produce (which is based on observation).
I have more than a basic understanding of the subject matter (neural networks; specifically transformers, etc.). It’s actually not a hugely technical field.
By the way, it appears that you are in category (a).
You don’t know what they’re able to produce because you clearly don’t know how they actually work. So your “observations” are not worth much.
1 reply →