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Has ChatGPT used highly rated and upvoted questions/answers from Stack Overflow in its training data?
For me it makes complete sense to take answers that have upwards of 100 upvotes and include them in your training data, but people around me seem to think this hypothesis doesn't make sense. Is there a way to confirm this?
ChatGPT is in the Large Language Models (LLM) category. The most (in)famous GPT model is probably GPT-3, because since then, researchers realized that LLMs mostly follow a predictable scaling law, thus the more data and the bigger model, the better. It is accurate to say that ChatGPT was trained with Stack Overflow data, but it should be all Stack Overflow instead of just most upvoted answers/comments.
The Wikipedia page of GPT-3 and their paper mentions that GPT-3 was trained on multiple datasets, and one of which is the Common Crawl, which basically crawls everything on the Internet. Some data pre-processing was done before training, but the authors did not mention removing the comments, so we can say that it is all Stack Overflow data.
If we look at the Common Crawl data in Sep 2022, there is indeed the domain com.stackoverflow in their list. Thus, while ChatGPT was trained on Stack Overflow data, it is trained on all Stack Overflow data instead of just most upvoted answers.
However, if you think ChatGPT's code output is of high quality, think again, because Stack Overflow temporarily bans ChatGPT because the average rate of getting correct answers from ChatGPT is too low, the posting of answers created by ChatGPT is substantially harmful to the site and to users who are asking and looking for correct answers (quoted from the link).
Here is the justification of them:
The primary problem is that while the answers which ChatGPT produces have a high rate of being incorrect, they typically look like they might be good and the answers are very easy to produce. There are also many people trying out ChatGPT to create answers, without the expertise or willingness to verify that the answer is correct prior to posting. Because such answers are so easy to produce, a large number of people are posting a lot of answers. The volume of these answers (thousands) and the fact that the answers often require a detailed read by someone with at least some subject matter expertise in order to determine that the answer is actually bad has effectively swamped our volunteer-based quality curation infrastructure.
EDIT: a comment below also confirmed that GPT-2, the predecessor of GPT-3 and ChatGPT, was trained with Stack Overflow data.
Technically speaking, we don't know whether ChatGPT used Stack Overflow data for the training. That is because ChatGPT is a proprietary model, and OpenAI hasn't published training details to the public.
We know that, from the GPT-3 paper [1], the GPT-3 training dataset includes Common Crawl. However, there is no indication of the relation between GPT-3.5 series (including ChatGPT) and GPT-3 models according to OpenAI's account.
See also this question Are GPT-3.5 series models based on GPT-3? for the discussion on GPT-3 vs. GPT-3.5.
I add: the token set is different between davinci (GPT-3) and text-davinci-003 (GPT-3.5) (source) thus the later cannot be a simple fine-tuning of the former.
I think, though, it is natural to infer OpenAI uses StackOverflow data because it is generally considered high quality [2] and easily obtainable. Also, if OpenAI ever uses web crawling data, it will see many StackOverflow and its clone sites.
it was trained using supervised from open ai team for initializing the conversational model and for more customize style, it can highly possible using chosen stackoverflow sample dataset since the answer of the question from the website was moderated. ChatGPT was take the advantage of GPT-3 model that has been trained from various corpus of data and can perform few-shot learning.
