5 ms·
LLMs don’t feel like a whole brain, they feel like the impulsive side that gets filtered by other processes.
by jcims 2y ago
LLMs don’t feel like a whole brain, they feel like the impulsive side that gets filtered by other processes.
- eightysixfour 2y agoWhile there is sometimes an exaggeration of the differences, I have always found LLMs to behave like (and have many of the weaknesses of) the left hemisphere of the brain as described in books like “The Master and His Emissary.” Considering the left is the language center, this shouldn’t be surprising.
- AdieuToLogic 2y ago> LLMs don’t feel like a whole brain ... Because they are not. LLM's are a better form of Bayesian inference[0] in that the generated tokens are statistically a better fit within a larger context. 0 - https://en.wikipedia.org/wiki/Bayesian_inference https://en.wikipedia.org/wiki/Bayesian_inference
- stavros 2y agoThis strikes me as saying "Brains aren't brains, they're merely a collection of neurons that output some voltage in response to their input voltage".
- suprjami 2y agoYou're comparing the physical item with the reasoning capabilities of that item. A popular LLM joke at the moment is "How many Rs in strawberry?" You, a human being, understand this question. The purpose of the activity is to count the letter R in that word and give the one correct answer. LLMs don't understand any of that. They don't know what words are, what letters are, what numbers are, what counting is, that a question can have one definite correct answer or many answers, what a question is, nor what an answer is. They break your input into tokens and then look at the most likely set of output tokens given your input. That's all. If you train a model on enough sensible input tokens and sensible responses then the output will mostly seem human and sensible. But it's never reasoning.
- gaganyaan 2y agoThey very much do understand words, letters, etc. Why do you think otherwise? Because of one trick question?
- suprjami 2y agoYou are fooled into thinking that producing a set of output tokens which mimic actual human input and output tokens displays understanding. It doesn't. "How many Rs in strawberry?" is not a trick question. That's about as straight and factual as a question could be.
- famouswaffles 2y ago>"How many Rs in strawberry?" is not a trick question. To a system that sees letters and words, sure. To a system that doesn't, you're just asking a blind man to count how many apples are on the table and patting yourself on the back for his failure. It just doesn't make any sense to begin with. And this is before the fact that human intelligence is rife with absurd seeming failure modes and cognitive biases. It's frankly very telling that these token related questions are the most popular kind of questions for these discussions. This is no testable definition of reasoning or intelligence that will cleanly separate LLMs and Humans. That is the reality today. And it should make anybody pause.
- kortilla 2y ago> you're just asking a blind man to count how many apples are on the table Which input is it you think an LLM doesn’t have to be able to count the number of letters in a word you literally provide it
- isaacfung 2y agoThe text is converted to embeddings after tokenization. The neural networwk only sees vectors. Imagine the original question is posed in English but it is translated to Chinese and then the LLM has to answer the original question based on the Chinese translation. It's a flaw of the tokenization we choose. We can train an LLM using letters instead of tokens as the base units but that would be inefficient.