Closed guotong1988 closed 5 years ago
Imho it's because of the dataset they have used.
It's huge and somewhat clean (they have taken specific text that had more than a specific score from reddit I believe, not sure). This means that in the dataset there were a lot example of QA (question-answer), TL;DR: (summarization), and so on.
Since the model is very good at learning patterns, if you set your input in a specific way (for example for summarization you concatenate TL;DR: at the end of the input), the model will recognize the pattern from the training dataset (TL;DR:) and try to generate sentences that follow the same pattern.
My observations are:
So imo, due to the various input it was trained on, it can't be perfect and there is a risk of unwanted output. I tried fine-tuning. Yes, it helps a lot to be on topic but only if your input follows your samples in a way as well. If not, your trained data will result in unwanted output then. Said so, I had not a lot data to train it with.
Thank you!
I'm not gonna lie. The part of my brain where i stored my philsophy minor is currently running around with its hair on fire. This thing is terrifying, and I'm not sure if I can articulate why.
Its like someone actually built Searles chinese room, and it started asking for a payrise.
Thank you!