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GPT-2: 6-Month Follow-Up

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Re: GPT-2: 6-Month Follow-Up

#81

Post by ptah »

newhaus1994 wrote: post #53
gambler wrote: post #28

"Cornell University is studying human susceptibility to digital disinformation generated by language models." "The Middlebury Institute of International Studies Center on Terrorism, Extremism, and Counterterrorism (CTEC) is exploring how GPT-2 could be misused by terrorists and extremists online." "The University of Oregon is developing a series of “bias probes” to analyze bias within GPT-2." But apparently no univer…

We (the Middlebury Institute's CTEC) are an extremism and terrorism research lab, and so we're tracking the ways that tech is used by terrorists and extremists. For a lot of nonstate orgs with sophisticated propaganda arms, an ideologically cohesive text generation capability would be a huge advantage in scaling up info ops. We are looking to measure whether or not GPT-2 or other neural text generators are useful for…

do you not consider the scenario described in your comment's parent to be worse than any terrorist scenario?

Re: GPT-2: 6-Month Follow-Up

#82

Post by The_rationalist »

inimino wrote: post #65
The_rationalist wrote: post #7

Are there any real use case for GPT-2? Does it solve any problem? I've read almost all state of the art leaderboards of all Nlp tasks of paperswithcode.com and truth is except text generation, openAI has not one state of the art, they are not even visible in leaderboards. OpenAI is maybe the AI research center with the biggest funding and comparatively to other well known (Microsoft, Facebook, Google or even zalando.…

> Most of the time it seems trivial to beat the old state of the art, just use BERT or XLnet on a task where nobody applied it before If it was a high-return fruit somebody would be doing it. Not necessarily publishing papers about it or trying to beat useless artificial benchmarks on it.

If it was a high-return fruit somebody would be doing it." Not necessarily.

Not necessarily publishing papers about it Yes.

trying to beat useless artificial benchmarks on it. Wtf is this bullshit? AI benchmarcks are what direct progress in AI and allow to quantify it. And they are less and less artificial and more and more real world: E.g quora, reddit, Wikipedia and Facebook datasets.

Re: GPT-2: 6-Month Follow-Up

#83

Post by enkiv2 »

postalrat wrote: post #54
comradesmith wrote: post #47

Earlier quoted context omitted.

http://talktotransformer.com Seems accessible to me

That isn't running the full model.

It's running the latest model to be released, which is 774M. Since the initial announcement, larger models have been released every few months, so we're on track to have the full model released by 2020. (This is what OP is literally about -- the roadmap for releasing larger subsets of the model.)

Re: GPT-2: 6-Month Follow-Up

#84

Post by enkiv2 »

gambler wrote: post #22

Hopefully someone will make a working demo of it, like Adam King did for 345M. People should be able to experiment with this stuff without relying on the hype of press releases: https://medium.com/@VictorBanev/interrogating-gpt-2-345m-aaf... Not sure why open AI doesn't do this themselves. That fully aligns with their stated mission.

It's not particularly hard to check out the source & run it on your own machine. For those who don't know how to use git or are afraid of the command line, there's TalkToTransformer.

Re: GPT-2: 6-Month Follow-Up

#85

Post by The_rationalist »

ivalm wrote: post #60
The_rationalist wrote: post #36

Earlier quoted context omitted.

Except that BERT is now obscoleted by https://github.com/zihangdai/xlnet (but xlnet would never have existed without BERT)

Kind of, there are a bunch of transformers that might perform better than BERT (Ernie 2.0 being stronger than xlnet, for example), but often this is a function of training size (xlnet trained on 10x more data than original BERT). Realistically there are now BERTs released finetuned for special corpa (biobert, clinical bert, etc) so if you want to work on those kind of texts you are better off starting with a BERT tha…

Well you comment was really interesting to me because I didn't know ERNIE 2.0 and it's concept of continual learning seems to be really a step forward!

But some of you statements seems incorrect: Ernie 2.0 being stronger than xlnet XLnet is the neural net with the biggest number of first places on benchmarck leaderboards. Cf: https://paperswithcode.com/paper/xlnet-generalized-autoregre... While ernie 2.0 has currently 0 first place on paperswithcode.com https://paperswithcode.com/paper/ernie-20-a-continual-pre-tr...

xlnet trained on 10x more data than original BERT No, I've read on a github issue of xlnet that xlnet base is same size as bert base and xlnet large is same size as bert large. (I don't know for ernie 2)

Well your point on finetuned bert vs non finetuned xlnet is interesting. ROBERTA is so fine tuned it beat XLnet on some tasks. But generally xlnet non finetuned beat BERT finetuned and there are more and more xlnet finetuned each week. But your point does apply for Roberta, and for the few tasks where bert as been applyed but xlnet hasn't yet.

Re: GPT-2: 6-Month Follow-Up

#86

Post by The_rationalist »

p1esk wrote: post #61
The_rationalist wrote: post #55

Earlier quoted context omitted.

Well it was just a belief. I may be wrong. I asked them by curiosity https://github.com/openai/gpt-2/issues/168 So we will know.

How do you think it could be used there? A separate model just for providing tags, or the same model but trained to predict tags as well?

I was imagining using a separate model just for providing tags as they are very accurate. It would theoretically give gpt-2 useful data.

GPT-2 has not (yet) been trained to predict POS tags to my knowledge, nor BERT, or ernie 2 or xlnet has, but I think they have great potential to improve POS accuracy.

Re: GPT-2: 6-Month Follow-Up

#87

Post by skybrian »

insulanus wrote: post #77
skybrian wrote: post #71

Earlier quoted context omitted.

It seems like this is missing the point of public data? When you make an edit to Wikipedia, anyone in the world can read it. You don't benefit when they read an article, but it doesn't cost you anything either. "Anyone" includes researchers. That's part of the deal. Yes, they benefit, but you aren't harmed. That's zero-sum thinking.

I think you are correct that nothing is taken away from an author when someone reads their Wikipedia article. Perhaps what the poster above you is saying is that there is a continuum of information, some more personal and sensitive, like your current location, and some less personal, like the Wikipedia article on Elephants. Taking data about specific humans, (or humans in general), and turning it into code that has p…

It doesn't cost you anything when someone uses your Reddit posts to train a model either. The supposed harm is very tenuous.

Re: GPT-2: 6-Month Follow-Up

#88

Post by newhaus1994 »

40four wrote: post #76
newhaus1994 wrote: post #53

Earlier quoted context omitted.

We (the Middlebury Institute's CTEC) are an extremism and terrorism research lab, and so we're tracking the ways that tech is used by terrorists and extremists. For a lot of nonstate orgs with sophisticated propaganda arms, an ideologically cohesive text generation capability would be a huge advantage in scaling up info ops. We are looking to measure whether or not GPT-2 or other neural text generators are useful for…

I think their point isn't that terrorists leveraging this tech not a problem. It is certainly a problem. But the greater problem being a few large entities being the only ones who have access to or control over it. I think it's pretty clear that terrorists or any other bad actor will find great value & utility in this tech. The article from OpenAI says 'Humans can be convinced by synthetic text.' & research at Cornel…

In spite of all that we're studying wrt abuse potential, I (and my team) generally support open-sourcing tech, and I hope that we can contribute not to "oh this is dangerous, don't release" but rather to "oh this is dangerous, it's already released, what are we going to do now?"

Re: GPT-2: 6-Month Follow-Up

#89

Post by newhaus1994 »

ptah wrote: post #81
newhaus1994 wrote: post #53

Earlier quoted context omitted.

We (the Middlebury Institute's CTEC) are an extremism and terrorism research lab, and so we're tracking the ways that tech is used by terrorists and extremists. For a lot of nonstate orgs with sophisticated propaganda arms, an ideologically cohesive text generation capability would be a huge advantage in scaling up info ops. We are looking to measure whether or not GPT-2 or other neural text generators are useful for…

do you not consider the scenario described in your comment's parent to be worse than any terrorist scenario?

Clearly. I also think that the pain of the centralization of tech like this will be felt in the scope of years, while the increase in the automation of propaganda and radicalization will be felt in the coming months.

Like I replied to the other poster, I strongly support open-sourcing tech. Centralization of tech like this helps exacerbate the problem: state and sophisticated nonstate groups have the resources to develop it indigenously, while the public can't dig into it and start developing a set of norms and best practices to approach detection and mitigation.

Re: GPT-2: 6-Month Follow-Up

#90

Post by ivalm »

The_rationalist wrote: post #85
ivalm wrote: post #60

Earlier quoted context omitted.

Kind of, there are a bunch of transformers that might perform better than BERT (Ernie 2.0 being stronger than xlnet, for example), but often this is a function of training size (xlnet trained on 10x more data than original BERT). Realistically there are now BERTs released finetuned for special corpa (biobert, clinical bert, etc) so if you want to work on those kind of texts you are better off starting with a BERT tha…

Well you comment was really interesting to me because I didn't know ERNIE 2.0 and it's concept of continual learning seems to be really a step forward! But some of you statements seems incorrect: Ernie 2.0 being stronger than xlnet XLnet is the neural net with the biggest number of first places on benchmarck leaderboards. Cf: https://paperswithcode.com/paper/xlnet-generalized-autoregre... While ernie 2.0 has currentl…

> xlnet trained on 10x more data than original BERT No, I've read on a github issue of xlnet that xlnet base is same size as bert base and xlnet large is same size as bert large. (I don't know for ernie 2)

It's not about the size of the model, but the training data. If you read the XLNet paper https://arxiv.org/pdf/1906.08237.pdf they clearly state in section 3.1:

"Following BERT [10], we use the BooksCorpus [41] and English Wikipedia as part of our pretraining data, which have 13GB plain text combined. In addition, we include Giga5 (16GB text) [23], ClueWeb 2012-B (extended from [5]), and Common Crawl [6] for pretraining. We use heuristics to aggressively filter out short or low-quality articles for ClueWeb 2012-B and Common Crawl, which results in 19GB and 78GB text respectively. After tokenization with SentencePiece [16], we obtain 2.78B, 1.09B, 4.75B, 4.30B, and 19.97B subword pieces for Wikipedia, BooksCorpus, Giga5, ClueWeb, and Common Crawl respectively, which are 32.89B in total"

If you compare to BERT paper https://arxiv.org/pdf/1810.04805.pdf training data for "pretraining data" section in also section 3.1:

"Pre-training data The pre-training procedure largely follows the existing literature on language model pre-training. For the pre-training corpus we use the BooksCorpus (800M words) (Zhu et al., 2015) and English Wikipedia (2,500M words). For Wikipedia we extract only the text passages and ignore lists, tables, and headers. It is critical to use a document-level corpus rather than a shuffled sentence-level corpus such as the Billion Word Benchmark (Chelba et al., 2013) in order to extract long contiguous sequences."

So 32.89B words for XLNet vs 3.3B words for BERT.

We've also run fine tuning experiments supplementing additional private medical corpus (~10B words) and felt starting from clinical-bert was better than xlnet (for our rather specific use cases).

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