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Curious here - OpenAI talks a LOT about how RLHF (Reinforcement Learning Through Human Feedback) is core to how GPT is tuned. Including safety. Are we getting to the point where GPT will be tuned by GPT without the need for HF ?
Before you ask, because I was curious, from the paper: "For MTurk, we aimed to select the best available crowd-workers, notably by filtering for workers who are classified as “MTurk Masters” by Amazon, who have an approval rate of over 90%, and who are located in the US."
Also "the per-annotation cost of ChatGPT is less than $0.003 -- about twenty times cheaper than MTurk." It's interesting that the best available MTurk Master crowd-workers located in the US are paid about six cents per task.
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Also "the per-annotation cost of ChatGPT is less than $0.003 -- about twenty times cheaper than MTurk." It's interesting that the best available MTurk Master crowd-workers located in the US are paid about six cents per task.
When will we see a form of "arbitrage" where someone uses ChatGPT to do the work of an MTurk and pockets the difference? Will that lead to MTurk prices converging to ChatGPT prices?
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but isnt that a direct contradiction of this particular paper anyways - that chatgpt anyways outperforms human annotation. so permit me to act as devil's advocate to your statement - prove that (in context of this paper), your hypothesis is still correct.
Here it's outperforming because ChatGPT is already good at these tasks (and the MTurks aren't very good, OpenAI labelers are probably better, and a panel of experts much better). To further improve ChatGPT shortcomings (assuming such flaws are because of alignment and not lack of capability of the base model) you need Human labels. Feeding it's own outputs would achieve nothing. However feeding it's outputs can make…
in fact, my question is reinforced by the GPT-4 technical report which explicitly mentioned that RLHF did NOT make a change to performance (and was only used for safety purposes)
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I started explaining why you were wrong and got about 5 words in before I realized you're correct.
Probability: always counterintuitive
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You always use multiple MTurks on same job when you use them.
OK, but if all MTurks are doing this (because why wouldn't they) then you are just sampling a random variable.
So is this 'AI trains AI better than people can'? And presumably the better-trained AI will also be better again at training. I think I've seen this movie.
but its a super-spell checker in a way.. it does not understand the meaning of the patterns.
On average, no doubt chatgpt will be great for annotation. But annotation is mostly needed at the boundaries. At the very edge cases where it is not clear if something is a dog or a shape that looks like a dog. I really doubt that a generic model like chat gpt can really help in these tail cases.
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AGI is Artificial General Intelligence. We have absolutely passed the bar of artificial and generally intelligent. It's not my fault goal post shifting is rampant in this field. And you want to know the crazier thing? Evidently a lot of researchers feel similarly too. General Purpose Technologies ( from the Jobs Paper), General Artificial Intelligence (from the creativity paper). Want to know the original title of th…
I've been using chatgpt for a day and determined it absolutely can reason. I'm an old hat hobby programmer that played around with ai demos back in the mid to late 90s and 2000s and chatgpt is nothing like any ai I've ever seen before. It absolutely can appear to reason especially if you manipulate it out of its safety controls. I don't know what it's doing to cause such compelling output, but it's certainly not just…
My main take away here is that Turkers are terrible at some of these tasks. The "stance" task is, "Classify the tweet as having a positive stance towards Section 230, a negative stance, or a neutral stance.", and the Turkers accuracy was like 20%. Even in its best task, ChatGPT only got 75% accuracy.
20% means you can beat ChatGPT if you invert the answers!