91–100 of 107 posts
I'd been having trouble with ones which had a reasonable logical flow, but didn't communicate a complete idea.
Of course, pretty small N so YMMV
Easy mode is cake. Hard mode is good enough that I'd like to see some sort of distance metric to the nearest real story, to be sure the model isn't accidentally copying truth.
Yeah, I ran into at least one example which basically regurgitated a real paper. The "fake" article was:
Efficient organic light-emitting diodes from delayed fluorescence
A class of metal-free organic electroluminescent molecules is designed in which both singlet and triplet excitons contribute to light emission, leading to an intrinsic fluorescence efficiency greater than 90 per cent and an external electroluminescence efficiency comparable to that achieved in high-efficiency phosphorescence-based organic light-emitting diodes.
Highly efficient organic light-emitting diodes from delayed fluorescence
Here we report a class of metal-free organic electroluminescent molecules in which the energy gap between the singlet and triplet excited states is minimized by design4, thereby promoting highly efficient spin up-conversion from non-radiative triplet states to radiative singlet states while maintaining high radiative decay rates, of more than 106 decays per second. In other words, these molecules harness both singlet and triplet excitons for light emission through fluorescence decay channels, leading to an intrinsic fluorescence efficiency in excess of 90 per cent and a very high external electroluminescence efficiency, of more than 19 per cent, which is comparable to that achieved in high-efficiency phosphorescence-based OLEDsEven on hard, if you understand the terminology, the fake ones are mostly gibberish.
If you don't understand the terminology, any paper is gibberish :) but I agree, I can detect fake ones fairly reliably in biology, but not in e.g. astronomy.
Only one was convincing enough to be truly challenging, I got it right because the mechanism proposed was fishy, 1) I had domain expertise, and 2) the date of the paper made no sense relative to when that sort of a discovery would be made (2009 is too early)
Quite easy when you know one is fake. Flagging fake articles in a review queue, by abstract only, and when none may exist all the way up to all being fake .... Now that's a challenge. Also, if you train GPT on the whole corpus of Nature / Science / whatever articles up to, say, 2005, could you feed it leading text about discoveries after 2005 and see if it hypothesizes the justification for those discoveries in the s…
I find the whole thing ominous because there is no "there" there: there is no understanding in the GPT-2 system, but it's able to generate increasingly plausible text. This greatly increases the amount of plausible nonsense that can be used to drown out actual research. You could certainly replace a lot of pop-sci and start several political movements with GPT-2... all of which has no actual nutritional content.
I was really impressed with gpt-2 but seeing this really gave me a feel for how much of a lack of understanding it has.
Cool demo. With these GPT models, I don't get the appeal of creating fake text that at best can pass as real to someone who doesn't understand the topic and context. What's the use case? Generating more believable spam for social media? Anything else? Because there's no real knowledge representation or information extraction going on here.
https://www.planet-9.com/threads/air-conditioning-compressor...
Screenshot including the spam post, in case it's removed: https://imgur.com/GgCkp1r
This is going to suck.
Sokal-as-a-Service
Quite easy when you know one is fake. Flagging fake articles in a review queue, by abstract only, and when none may exist all the way up to all being fake .... Now that's a challenge. Also, if you train GPT on the whole corpus of Nature / Science / whatever articles up to, say, 2005, could you feed it leading text about discoveries after 2005 and see if it hypothesizes the justification for those discoveries in the s…
I find the whole thing ominous because there is no "there" there: there is no understanding in the GPT-2 system, but it's able to generate increasingly plausible text. This greatly increases the amount of plausible nonsense that can be used to drown out actual research. You could certainly replace a lot of pop-sci and start several political movements with GPT-2... all of which has no actual nutritional content.
Easy mode is cake. Hard mode is good enough that I'd like to see some sort of distance metric to the nearest real story, to be sure the model isn't accidentally copying truth.
> Hard mode is good enough that I'd like to see some sort of distance metric to the nearest real story, to be sure the model isn't accidentally copying truth. Yeah, I ran into at least one example which basically regurgitated a real paper. The "fake" article was: Efficient organic light-emitting diodes from delayed fluorescence A class of metal-free organic electroluminescent molecules is designed in which both singl…
Knowing how they're generated, a sequence of sentences that make sense are likely copied almost verbatim from an article written by a human. Without understanding the concepts, the algorithm may simply repeat words that go well together - and what goes together better than sentences that were written together in the first place?
What the GPT model is really good at is at is identifying when a sentence makes sense in the current context. Given that it has half of the internet as its learning corpus, it is easy that it's simply returning a piece of text that we do not know about. The real achievement thus is finding ideas that are actually appropriate in relation to their input text.