5 ms·

- GPT style language models try to build a model of the world: https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 - GPT style language models en

- GPT style language models try to build a model of the world: https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 - GPT style language models end up internally implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples): https://arxiv.org/abs/2212.10559 https://arxiv.org/abs/2212.10559 There's more. People are just in denial.

Godel_unicode 4y ago
Your second bullet isn’t what the paper says. It says that the model’s accuracy under in-context learning from the meta gradients is similar to manual fine-tuning with back propagation (under some surprising constraints in the name of “fairness”). The language model isn’t implementing anything, you have the directionality wrong. GPT models are constructed with pretrained gradients which are applicable in a large set of situations. It’s just an optimization technique, albeit a clever one. Quoting from the paper: In summary, we explain ICL as a process of meta-optimization: (1) a Transformer-based pre- trained language model serves as a meta-optimizer; (2) it produces meta-gradients according to the demonstration examples through forward computa- tion; (3) through attention, the meta-gradients are applied to the original language model to build an ICL model.
- kilgnad 4y ago
  What you quoted from the paper literally said what I said. There's no difference. The forward computation is computing gradients. These new gradients are applied to the model, to build the icl model via "attention". What I said: "implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples):" Perhaps you're not getting it. Forward computation is the neural network processing an input. The paper is saying gradients are built here. These new gradients are then applied through the transformers "attention" step. The icl model is the model with the attention applies. It is literally just another perspective of what transformers do.
  - deleted 4y ago
    [deleted]
  - Godel_unicode 4y ago
    You said “… models end up internally implementing…”. This is incorrect and also importantly different than what you just said. The model and the training using ICL are different things, which it appears you are now beginning to understand. Precision in speech is critical when discussing complex subjects.
    - kilgnad 4y ago
      Pedantic arguments hinder useful discussion. That's what you're doing here. It took me a couple minutes to "figure out" just what you mean and even now I don't fully get it. Are you in actuality complaining about the word "implement"? You're pedantically arguing that the word doesn't belong? That's the least ludicrous intent for all the possible intents behind your reply. Even your pedantism can't win here though. You are in fact wrong, implement is an appropriate word here, the only thing I understand here is how mistaken you are.
      - Godel_unicode 4y ago
        You might want to do some research into the words “science” and “pedantry”, since the former is just meticulous application of the latter. Calling a scientist pedantic is a compliment. That aside, you’ve been arguing that these models understand things and citing these papers as evidence. They are not. They are evidence that the ability of these models to generate text based off of their existing training set can easily be finetuned in a number of ways to add training sets after the initial zero-shot learning. That’s all these models do, they generate text based upon some training set. If we define understanding as the ability to extrapolate beyond what one has been told, they are expressly not doing that. Your papers explain this quite well. Edit: to see more concrete examples of this, look into the unfortunately named “hallucination” ability of LLMs. Once you realize that they only know what they were told and are unable to logically extrapolate the point becomes clearer. I hope that helps.

Godel_unicode 4y ago

Your second bullet isn’t what the paper says. It says that the model’s accuracy under in-context learning from the meta gradients is similar to manual fine-tuning with back propagation (under some surprising constraints in the name of “fairness”). The language model isn’t implementing anything, you have the directionality wrong. GPT models are constructed with pretrained gradients which are applicable in a large set of situations. It’s just an optimization technique, albeit a clever one. Quoting from the paper: In summary, we explain ICL as a process of meta-optimization: (1) a Transformer-based pre- trained language model serves as a meta-optimizer; (2) it produces meta-gradients according to the demonstration examples through forward computa- tion; (3) through attention, the meta-gradients are applied to the original language model to build an ICL model.

kilgnad 4y ago
What you quoted from the paper literally said what I said. There's no difference. The forward computation is computing gradients. These new gradients are applied to the model, to build the icl model via "attention". What I said: "implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples):" Perhaps you're not getting it. Forward computation is the neural network processing an input. The paper is saying gradients are built here. These new gradients are then applied through the transformers "attention" step. The icl model is the model with the attention applies. It is literally just another perspective of what transformers do.
- deleted 4y ago
  [deleted]
- Godel_unicode 4y ago
  You said “… models end up internally implementing…”. This is incorrect and also importantly different than what you just said. The model and the training using ICL are different things, which it appears you are now beginning to understand. Precision in speech is critical when discussing complex subjects.
  - kilgnad 4y ago
    Pedantic arguments hinder useful discussion. That's what you're doing here. It took me a couple minutes to "figure out" just what you mean and even now I don't fully get it. Are you in actuality complaining about the word "implement"? You're pedantically arguing that the word doesn't belong? That's the least ludicrous intent for all the possible intents behind your reply. Even your pedantism can't win here though. You are in fact wrong, implement is an appropriate word here, the only thing I understand here is how mistaken you are.
    - Godel_unicode 4y ago
      You might want to do some research into the words “science” and “pedantry”, since the former is just meticulous application of the latter. Calling a scientist pedantic is a compliment. That aside, you’ve been arguing that these models understand things and citing these papers as evidence. They are not. They are evidence that the ability of these models to generate text based off of their existing training set can easily be finetuned in a number of ways to add training sets after the initial zero-shot learning. That’s all these models do, they generate text based upon some training set. If we define understanding as the ability to extrapolate beyond what one has been told, they are expressly not doing that. Your papers explain this quite well. Edit: to see more concrete examples of this, look into the unfortunately named “hallucination” ability of LLMs. Once you realize that they only know what they were told and are unable to logically extrapolate the point becomes clearer. I hope that helps.