8 ms·

When I said it operates on a level of meaning, I refer to its ability to perform transformations like this: H: translate the following sentence to instead us

• YeGoblynQueenne 4y ago
  That's amazing and all, but it's also speculation, yes? It's how you imagine, or guess, that things are happening. It's not like you, or anyone else, knows that this kind of thing is really happening. Moreover, there isn't any clear reason why you need to speculate all that, in order to explain the observed behaviour of Large Language Models (LLMs). You say you don't think LLMs "can perform this kind of translation by merely comparing tokens statistically tending to be near one another". And yet, that's what we know that LLMs do, because that's what they are made to do [edit: they do what you describe after the "by merely"]. Why do we need to imagine some other, so far unobserved, process at play? What is the motivation? What is the justification? Why do you think LLM's can't "perform this kind of translation by merely comparing tokens statistically tending to be near one another". It's clear what you are saying, but, why? Why are you saying that? Why should we assume that the well-known and understood design and functionality of LLMs is not enough to explain their behaviour? Is it because we haven't observed such behaviour before and some people are surprised by it? Well, we didn't have as large language models before, so obviously we didn't have a chance to observe their behaviour first-hand, but we also didn't have cars that could go 100 miles an hour, until we did, and making a faster car is no reason to be surprised, or wonder "how does it do it if it's just a car?".
  • knome 4y ago
    >That's amazing and all It earnestly is amazing. >Why do you think LLM's can't "perform this kind of translation by merely comparing tokens statistically tending to be near one another". The implication I gather from those dismissing this technology as "merely comparing tokens statistically tending to be near one another" is that they think the whole of the LLM is nothing but a trick, a sleight of hand where it generates output token by token Markov style after consuming enough text, just using some percent chance of tokens following on after another. In my example, it generated the metaphor "sweeping the ice" as a replacement for "spinning my tires". Both are using in context to imply a level of busywork associated with the respective contexts. To generate such a transformation requires understanding this inner meaning and associating it. It requires a level of creativity to determine that "sweeping the ice" in curling is an appropriate replacement. You cannot rearrange "spinning my tires" into "sweeping the ice" without having derived and translated the underlying meaning. There is nothing to connect the two sets of tokens except the metaphoric intent. >Why should we assume that the well-known and understood design and functionality of LLMs is not enough to explain their behaviour? Obviously LLMs are capable of this as it is an LLM that is producing it. I am not denying that the inputs and outputs are tokens marked up with distance information, nor that the process of generating tokens is just a bunch of vector multiplication to generate neuron values from weights across the layers of the network. I am suggesting that with sufficient training, layers and parameters, that the neural net must be forced into a configuration that allows it to perform this meaning/intent based processing as gradient descent pushes and pulls its the network along the twelve thousand or so dimensions GPT-3 uses. Undoubtedly GPT-4 and soon 5 use even more. LLMs have repeatedly been found to have developed novel and unintended capabilities as their complexity has increased. Why do you think that LLMs shouldn't be capable of manipulating on the level of meanings and intents rather than just rearranging tokens?
• deleted 4y ago
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• knome 4y ago
  [ this comment was written in response to a user who appears to have deleted their comment before I could finish putting my response together. I'm not sure why they deleted it, it seemed reasonable in the face of the ongoing discussion in this thread. I'm going to leave my response here, partly so they can see it if so interested, and mostly just because I already spent the time to write it :) ] I fail to see any distinction between your description of "mapping terms to latent spaces" and my assertion of GPT "working at a level of meaning and intent". I fail to see any distinction between "It's using complex non-linear transformations to map observed text to an extremely high-dimensional latent space, and in addition learning to both encode text to this representation and decode it from this representation back to the source language" and my assertion that "the literal meanings are abstracted and re-encoded". I feel like you're disagreeing with my stated positions because I am expressing them in a lay anthropomorphic language to express its functioning, but that we're otherwise agreeing? I had initially been responding to the user dvt, who said >LLMs work on tokens, which, for the most part, are words (longer words are generally split up into multiple tokens). Using concepts like "meaning" and "intent" muddies the waters unnecessarily. this seemed like an incorrect characterization of the process as I understood it. he doesn't acknowledge the existence of what you refer to as latent spaces, and seems fixated on GPT as merely moving around tokens (though I may be reading his position incorrectly, of course). I was arguing a claim of GPT's ability to represent concept distinct from literal meanings of terms, and for "understanding" how to transform these abstract meanings into different contexts. As far as I can tell, I don't really disagree with what you're saying at all.