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> Where would a language model find the world model? Where would it store it? And why would it even need it? It is pattern recognition with many layers of abst

by xcv123 3y ago

> Where would a language model find the world model? Where would it store it? And why would it even need it? It is pattern recognition with many layers of abstraction. Obviously it will infer semantic relations at some level. It is a type of machine learning. The entire point of machine learning is to generate a model of the data which can be generalized to new inputs. It would find the world model in semantic relations in text. It would store it in its vast neural network. It would need a world model in order to perform natural language understanding tasks, which is exactly what it was designed to do. When asked about the world it needs a model of the world in order to generate useful answers. GPT-4 took months to train on a supercomputer and it generated a neural network of hundreds of gigabytes. What exactly was that supercomputer doing for several months and what exactly would the neural network represent if not a world model? Are you thinking it rote learned answers to every possible question? > so it seems likely that a world model must be developed first It is "a" model. It can be completely alien to whatever exists in a human mind but still function and exist as some type of model. > Why would it ever be possible to derive the representation just from the pointer? It only needs to have a sufficient representation of the Sea that is useful for the tasks we have trained it to do. It doesn't need to derive "the" one true representation that humans have of the Sea, whatever that is.

YeGoblynQueenne 3y ago
>> GPT-4 took months to train on a supercomputer and it generated a neural network of hundreds of gigabytes. What exactly was that supercomputer doing for several months and what exactly would the neural network represent if not a world model? I believe GPT-4 was trained on a server farm, not a single computer. In any case what it was doing all that time was going over and over the text in its gigantic training corpus, which was of petabyte size, and optimising the objective P(tₖ|tₖ₋ₙ , ..., tₖ₋₁) i.e. the probability of token k given a "sliding window" of the n preceding (or surrounding) tokens. There is nothing in this objective that needs a world model, and it is really not obvious why optimising this objective should lead to development of a world model, rather than, or in addition to, a model of the training corpus. It is easy to see how this stuff works. You can train your own language model easily, although of course it would have to be a smaller language model. For example, you can train a Hidden Markov Model on the text of freely available literary works on project Guttenberg, or on wikipedia pages, and without too much compute (an ordinary laptop will do). In fact, I recommend that as an exercise and as an experiment to gain a better understanding in how language modelling works, for those who are curious about questions regarding their ability to model something beyond text. A good textbook to begin with statistical language modelling is "Foundations of Statistical Natural Language Processing" by Manning and Schűtze: https://nlp.stanford.edu/fsnlp/ https://nlp.stanford.edu/fsnlp/ Or, just as good, "Speech and Language Processing" by Jurafsky and Martin: https://web.stanford.edu/~jurafsky/slp3/ https://web.stanford.edu/~jurafsky/slp3/ Or, if you don't have the time for an entire textbook, "Statistical Language Learning" by Eugene Charniak is an excellent, concise introduction to the subject: https://archive.org/details/statisticallangu0000char https://archive.org/details/statisticallangu0000char
- xcv123 3y ago
  > I believe GPT-4 was trained on a server farm, not a single computer. Yes a server farm of Nvidia A100 with supercomputing performance. > It is easy to see how this stuff works. .. you can train a Hidden Markov Model.. No. GPT is not a Markov model.
  - YeGoblynQueenne 3y ago
    A HMM is one way to train a language model that optimises the conditional objective I note above. A transformer is another. Working with a HMM will give you an insight into how language modelling works, and it's something you can do easily and cheaply, unlike training a giant transformer architecture.
    - xcv123 3y ago
      Those primitive obsolete models are not the subject of the article. I already have a degree in applied mathematics and computer science and know the basics of machine learning. My questions to you were rhetorical. I wasn't asking you for academic guidance.
      - YeGoblynQueenne 3y ago
        I've already posted in another comment that I don't think there's a reason for me to insist, but I'd just like to point out that it's OK to not be an expert in everything but one should not have strong opinions on matters not of one's expertise. It's not a shame to not know everything, but it's unseemly to act like one does.
        xcv123 3y ago
        The exact same would apply to you. Projection. You are extremely arrogant with your opinions that no one else really agrees with and you talk down to people who disagree. Essentially you are insisting that your own personal definition of "world model" is true and the normal meaning of the term is not. Your argument is totally subjective and philosophical.

by xcv123 3y ago

YeGoblynQueenne 3y ago

>> GPT-4 took months to train on a supercomputer and it generated a neural network of hundreds of gigabytes. What exactly was that supercomputer doing for several months and what exactly would the neural network represent if not a world model? I believe GPT-4 was trained on a server farm, not a single computer. In any case what it was doing all that time was going over and over the text in its gigantic training corpus, which was of petabyte size, and optimising the objective P(tₖ|tₖ₋ₙ , ..., tₖ₋₁) i.e. the probability of token k given a "sliding window" of the n preceding (or surrounding) tokens. There is nothing in this objective that needs a world model, and it is really not obvious why optimising this objective should lead to development of a world model, rather than, or in addition to, a model of the training corpus. It is easy to see how this stuff works. You can train your own language model easily, although of course it would have to be a smaller language model. For example, you can train a Hidden Markov Model on the text of freely available literary works on project Guttenberg, or on wikipedia pages, and without too much compute (an ordinary laptop will do). In fact, I recommend that as an exercise and as an experiment to gain a better understanding in how language modelling works, for those who are curious about questions regarding their ability to model something beyond text. A good textbook to begin with statistical language modelling is "Foundations of Statistical Natural Language Processing" by Manning and Schűtze: https://nlp.stanford.edu/fsnlp/ https://nlp.stanford.edu/fsnlp/ Or, just as good, "Speech and Language Processing" by Jurafsky and Martin: https://web.stanford.edu/~jurafsky/slp3/ https://web.stanford.edu/~jurafsky/slp3/ Or, if you don't have the time for an entire textbook, "Statistical Language Learning" by Eugene Charniak is an excellent, concise introduction to the subject: https://archive.org/details/statisticallangu0000char https://archive.org/details/statisticallangu0000char

xcv123 3y ago
> I believe GPT-4 was trained on a server farm, not a single computer. Yes a server farm of Nvidia A100 with supercomputing performance. > It is easy to see how this stuff works. .. you can train a Hidden Markov Model.. No. GPT is not a Markov model.
- YeGoblynQueenne 3y ago
  A HMM is one way to train a language model that optimises the conditional objective I note above. A transformer is another. Working with a HMM will give you an insight into how language modelling works, and it's something you can do easily and cheaply, unlike training a giant transformer architecture.
  - xcv123 3y ago
    Those primitive obsolete models are not the subject of the article. I already have a degree in applied mathematics and computer science and know the basics of machine learning. My questions to you were rhetorical. I wasn't asking you for academic guidance.
    - YeGoblynQueenne 3y ago
      I've already posted in another comment that I don't think there's a reason for me to insist, but I'd just like to point out that it's OK to not be an expert in everything but one should not have strong opinions on matters not of one's expertise. It's not a shame to not know everything, but it's unseemly to act like one does.
      - xcv123 3y ago
        The exact same would apply to you. Projection. You are extremely arrogant with your opinions that no one else really agrees with and you talk down to people who disagree. Essentially you are insisting that your own personal definition of "world model" is true and the normal meaning of the term is not. Your argument is totally subjective and philosophical.