My point is that saying an LLM understands anything is anthropomorphizing the LLM and leads people into thought patterns that give it an inordinate amount of authority because people equate the simulacrum of understanding/comprehension with actual understanding.
I think we just fundamentally disagree on the concept of llms a being able to understand a topic rather than it being a shallow statistical prediction if the correct answer, and I just can’t equate understanding with statistical predictions. The fact that the underlying math is able to generalize the prediction in novel ways lends weight to the misbelief that it understands concepts, but the decoherence that happens over long conversations should shatter the illusion.
That’s fair. I actually don’t think we disagree that much - I just think I have trouble conveying what I am trying to say. Whenever someone talks about ‘shallow statistical predictions’, I think about older techniques like Statistical Machine Translation which even had trouble with things like word order, LLMs handle text on a higher level of abstraction (which I described as a form of textual understanding) - and hence handle things like word order better - but are still inherently statistical predictors. The model stores info about how words interact and relate to one another, but it does not ‘understand’ what the words actually (physically?) represent beyond these interactions nor does it ‘understand’ what it is doing. Albeit, those interactions are modeled well enough to give a convincing replica of doing so.
That makes more sense, thanks for expanding on your point.
Like I said, I mainly take issue with describing it as ‘understanding’ due to the connotations it gives off. I’m used to AI glazers using the same wordings and actually try to make the argument there is an understanding behind the statistical probabilities.
My point is that saying an LLM understands anything is anthropomorphizing the LLM and leads people into thought patterns that give it an inordinate amount of authority because people equate the simulacrum of understanding/comprehension with actual understanding.
I think we just fundamentally disagree on the concept of llms a being able to understand a topic rather than it being a shallow statistical prediction if the correct answer, and I just can’t equate understanding with statistical predictions. The fact that the underlying math is able to generalize the prediction in novel ways lends weight to the misbelief that it understands concepts, but the decoherence that happens over long conversations should shatter the illusion.
That’s fair. I actually don’t think we disagree that much - I just think I have trouble conveying what I am trying to say. Whenever someone talks about ‘shallow statistical predictions’, I think about older techniques like Statistical Machine Translation which even had trouble with things like word order, LLMs handle text on a higher level of abstraction (which I described as a form of textual understanding) - and hence handle things like word order better - but are still inherently statistical predictors. The model stores info about how words interact and relate to one another, but it does not ‘understand’ what the words actually (physically?) represent beyond these interactions nor does it ‘understand’ what it is doing. Albeit, those interactions are modeled well enough to give a convincing replica of doing so.
That makes more sense, thanks for expanding on your point.
Like I said, I mainly take issue with describing it as ‘understanding’ due to the connotations it gives off. I’m used to AI glazers using the same wordings and actually try to make the argument there is an understanding behind the statistical probabilities.
@Passerby6497 @8uurg #imho people believe #computers are #god like perfect machines, free of any #errors nothing could be further from the truth, basically every #cpu because of its complexity has #errors or even #security #flaws that need to be corrected afterwards via #software #microcode #updates yes current #llm #ai does not understand anything? It is just very good at guessing the next token to output?