Testing The Limits of Grover The Neural Fake News Detector. Can It Write Fiction? Can It Write Riddles?

Grover is a neural network modeled after GPT-2 as a state-of-the-art detector for Neural Network fake news. Grover is also a state-of-the-art generator of fake news and they provide a web interface. Since Grover is modeled after the full-size 1.5B GPT-2 – not the smaller version the public has access to – this is a bit like getting a back door to the full-size GPT-2. This is very exciting! (Update: They just reduced the size of the public model to 345M — everything in this post come from the full size model. Glad I stayed up way too late hammering the real thing for samples… Update 2: It’s back up!)

Grover was trained on 5000 news domains indexed by Google News, not the internet generally like GPT-2. As you would expect Grover excels at writing fake news:

Scientists Recommend Against Vaccination: "The Evidence Is Now Clear: Vaccines Cause Autism and Cancer"

After a 15-year study, scientists in Britain are recommending that the British government refrain from administering vaccines. The study, written by Mike Gunton, professor of epidemiology and professor of genetic medicine at the University of Liverpool, claims that the chances of the MMR vaccine, a vaccine for measles, mumps and rubella, causing autism in children are “strongly possible.” And while Gunton and his team say their findings only apply to the MMR vaccine, they believe there are “many similar disorders” linked to vaccines and there is “strong evidence” vaccines cause autism and leukemia. Gunton told the Telegraph that no effort should be made to give vaccines to children younger than 3 months of age. The vaccine is highly controversial, and parents have been choosing to opt out of the MMR vaccination in recent years. This year, the British government’s vaccination program chose not to distribute the MMR vaccine, citing the study as the reason.

You don’t have to fish to get coherent fake news of Grover, it’s absolutely great at it.

What else can we do with Grover?

Grover writes news when you ask it to generate the Article field, but since it will do any of the fields (title, authors, headline, etc) you can use Grover in other ways. Ask Grover to write a headline and give it the article text as input and it works kind of like a one-sentence summarizer (even when given straight up prose, more on this later…) Or estimate the date of a news story by asking Grover just generate the date field. Pretty cool.

But now let’s turn the power of this fully operational 1.5 Billion Parameter Neural Network to… other things.

Can Grover write silly fake news?

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My Best Friend Is A Monkey, How Can This Be? Terrible Neural Network Riddles

Can a neural network write good riddles? Maybe, but I sure didn’t prove it with this post. If you have a taste for the terrible, read on:

The too obvious:

The What Is It Riddle
What’s the first letter of the word ‘E’?

Answer: E.

The Why Does It Look Like A Man Riddle:
Why does it look like a man’s head?

Answer: It might be a man’s head.

The mysterious:

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One Thousand And One Neural Network Nights

Samples from the GPT-2 neural network are generally short – a few paragraphs – because it can only write 3 or 4 paragraph of text in a single sample. (This is vastly better than earlier networks like char-rnn).

I wanted to try out GPT-2 creating a single unbroken sample by feeding each sample into the next over and over again, on the vanilla GPT-2, just to see where it went.

I discovered that the bane of this neural network is a list. With the default 345M model almost every single run ended in an infinite list (Bible verses, Roman Numerals, vaguely sequential numbers.) In between there were a few megabytes of climate speeches, but everything ended in numbers staitons. May do a ‘absurdly long lists’ posts later. But if you need to defeat an evil robot powered by the GPT-2 neural network don’t go with the classic approach of “This statement is a lie.” Start a list because once a neural network stats counting IT CAN NOT STOP.

I still wanted to try a longer sample. One Thousand And One Nights is sort of a single story, sort of a series of short stories. Meandering narratives, asides, stories inside stories – story told by design to never end – it already sounds a lot like what you get out of a neural network! So I began with first paragraph of One Thousand and One Nights.

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Scenes That Never Happened In The The Web Serial WORM

Worm is a web serial written by Wildbow. You can read it on
parahumans.wordpress.com. If you have not read Worm turn away now because even silly neural network bits will spoiler you.

If you insist on reading anyway for the love of God at least only look at the first half the blog post. The second half contains paragraphs of text from Worm itself – a greatest hits of spoilers.

These are scenes generated by the GPT-2 neural network. The first section has unconditional scenes – where the network is just told ‘write something’; the second section has prompted scenes – where the network is given an existing Worm scene and asked to complete it.

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I Forced A Bot To Watch Over 1,000 Hours Star Trek Episodes And Then Asked It To Write 1000 Olive Garden Commercials.

I wish I could tell you I had a good reason why.

Anyway let’s use the GPT-2 345M model to recreate the viral (but not real) “I Forced a Bot” tweets that I named this site after… but with a trained model based on Star Trek.

I was going to do many different training materials and cover more of the original viral tweets, but the Star Trek Olive Garden Commercial samples are just killing me by themselves. I honestly think I could do nothing with GPT-2 but generate Olive Garden commercials from different models and never get bored. It deserves it’s own post!

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