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In context learning is already implicit finetuning. https://arxiv.org/abs/2212.10559 https://arxiv.org/abs/2212.10559. It's very questionable to what extent con

In context learning is already implicit finetuning. https://arxiv.org/abs/2212.10559 https://arxiv.org/abs/2212.10559. It's very questionable to what extent continuous training is necessary past a threshold of intelligence. Memory Augmented Large Language Models are Computationally Universal https://arxiv.org/abs/2301.04589 https://arxiv.org/abs/2301.04589

Quarrelsome 4y ago
> It's very questionable to what extent continuous training is necessary past a threshold of intelligence. To absorb new information about current events, otherwise they will always be time-locked into the past until a new dev cycle completes.
- famouswaffles 4y ago
  The point I'm trying to make is that you don't need continuous training to absorb new information about current events
time_to_smile 4y ago
Very interesting paper, thanks for the link!
kristjansson 4y ago
In context learning may act like fine tuning, but crucially does not mutate the state of the system. The same model prompted with the same task thousands of times is no better at it the thousandth time than the first.
- famouswaffles 4y ago
  GPT-3 is horrible at arithmetic. Yet if you define the algorithmic steps to perform addition on 2 numbers, accuracy on addition arithmetic shoots up to 98% even on very large numbers. https://arxiv.org/abs/2211.09066 https://arxiv.org/abs/2211.09066 Think about what that means. "Mutating the system" is not a crucial requirement at all. In context learning is extremely over-powered.
  - mr_toad 4y ago
    > Yet if you define the algorithmic steps to perform addition on 2 numbers You’re limited by the prompt size, which might be fine for simple arithmetic.
  - Jensson 4y ago
    > Think about what that means. It means you have natural language programming. We would need to prove that natural language programming is more powerful than traditional programming at solving logical problems, I haven't seen such a proof.
  - dragonwriter 4y ago
    > Yet if you define the algorithmic steps to perform addition on 2 numbers, accuracy on addition arithmetic shoots up to 98% even on very large numbers. https://arxiv.org/abs/2211.09066 https://arxiv.org/abs/2211.09066 Think about what that means. That means that even with the giant model, you need to stuff even the most basic knowledge for dealing with problems of that class into the prompt space to get it to work, cutting into conversation depth and per-response size? The advantage of GPT-4’s big window and the opportunity it provides for things like retrieval and deep iterative context shrinks if I’ve got to stuff a domain textbook into the system prompt so it isn’t just BSing me.

Quarrelsome 4y ago

> It's very questionable to what extent continuous training is necessary past a threshold of intelligence. To absorb new information about current events, otherwise they will always be time-locked into the past until a new dev cycle completes.

famouswaffles 4y ago
The point I'm trying to make is that you don't need continuous training to absorb new information about current events

time_to_smile 4y ago

Very interesting paper, thanks for the link!

kristjansson 4y ago

In context learning may act like fine tuning, but crucially does not mutate the state of the system. The same model prompted with the same task thousands of times is no better at it the thousandth time than the first.

famouswaffles 4y ago
GPT-3 is horrible at arithmetic. Yet if you define the algorithmic steps to perform addition on 2 numbers, accuracy on addition arithmetic shoots up to 98% even on very large numbers. https://arxiv.org/abs/2211.09066 https://arxiv.org/abs/2211.09066 Think about what that means. "Mutating the system" is not a crucial requirement at all. In context learning is extremely over-powered.
- mr_toad 4y ago
  > Yet if you define the algorithmic steps to perform addition on 2 numbers You’re limited by the prompt size, which might be fine for simple arithmetic.
- Jensson 4y ago
  > Think about what that means. It means you have natural language programming. We would need to prove that natural language programming is more powerful than traditional programming at solving logical problems, I haven't seen such a proof.
- dragonwriter 4y ago
  > Yet if you define the algorithmic steps to perform addition on 2 numbers, accuracy on addition arithmetic shoots up to 98% even on very large numbers. https://arxiv.org/abs/2211.09066 https://arxiv.org/abs/2211.09066 Think about what that means. That means that even with the giant model, you need to stuff even the most basic knowledge for dealing with problems of that class into the prompt space to get it to work, cutting into conversation depth and per-response size? The advantage of GPT-4’s big window and the opportunity it provides for things like retrieval and deep iterative context shrinks if I’ve got to stuff a domain textbook into the system prompt so it isn’t just BSing me.