I really don’t think that AI dungeon was the source of this idea (why do you think that?)
We’ve heard the story from a variety of sources all pointing to AI Dungeon, and to the fact that the idea was kept from spreading for a significant amount of time. This @gwern Reddit comment, and previous ones in the thread, cover the story well.
And even granting the claim about chain of thought, I disagree about where current progress is coming from. What exactly is the significant capability increase from fine-tuning models to do chain of thought? This isn’t part of ChatGPT or Codex or AlphaCode. What exactly is the story?
Regarding the effects of chain of thought prompting on progress[1], there’s two levels of impact: first order effects and second order effects.
On first order, once chain of thought became public a large number of groups started using it explicitly to finetune their models.
Aside from non-public examples, big ones include PaLM, Google’s most powerful model to date. Moreover, it makes models much more useful for internal R&D with just prompting and no finetuning.
We don’t know what OpenAI used for ChatGPT, or future models: if you have some information about that, it would be super useful to hear about it!
On second order: implementing this straightforwardly improved the impressiveness and capabilities of models, making them more obviously powerful to the outside world, more useful for customers, and leading to an increase in attention and investment into the field.
Due to compounding, the earlier these additional investments arrive, the sooner large downstream effects will happen.
We’ve heard the story from a variety of sources all pointing to AI Dungeon, and to the fact that the idea was kept from spreading for a significant amount of time. This @gwern Reddit comment, and previous ones in the thread, cover the story well.
Regarding the effects of chain of thought prompting on progress[1], there’s two levels of impact: first order effects and second order effects.
On first order, once chain of thought became public a large number of groups started using it explicitly to finetune their models.
Aside from non-public examples, big ones include PaLM, Google’s most powerful model to date. Moreover, it makes models much more useful for internal R&D with just prompting and no finetuning.
We don’t know what OpenAI used for ChatGPT, or future models: if you have some information about that, it would be super useful to hear about it!
On second order: implementing this straightforwardly improved the impressiveness and capabilities of models, making them more obviously powerful to the outside world, more useful for customers, and leading to an increase in attention and investment into the field.
Due to compounding, the earlier these additional investments arrive, the sooner large downstream effects will happen.
This is also partially replying to @Rohin Shah ’s question in another comment: