I built this a few weeks ago to help me read a neuroscience paper, and it works pretty well! I didn't fine-tune GPT-3, but I do take a lot of context from the paper and feed that into the prompt (not the whole paper).
Ppl have uploaded AI, biology, economics, philosophy papers and even law documents. Works in Chinese, Japanese, Spanish, French and more as well!
Those law documents - that is probably us :)
Can you comment a bit on fine-tuning options available at the user-level. There were cases (~15%) where it summarized is exactly on the wrong end of the spectrum (disclose within 3 days for example, came out as disclose post-3-days).
E.g. to test context I asked it "what is EtF"? There is no such acronym in the paper, but they mention English-to-French and that was the answer.
So how do you use GPT-3 for that? Do I understand correctly that the papers are to big to fit into its input window but you need to still take it as a whole into consideration? Is part of creating such service engineering some input prompt which is concatenated with what user writes? Using the playground I always found the input window size to be a huge limitation.
Looks cool though will try looking from Desktop…
However, for my sample size of 1 (my paper, https://www.sciencedirect.com/science/article/pii/S002186932... ), it didn't really produce anything useful, and was actively misleading quite a bit of the time. Of course, maybe I'm a bad writer -- I chose this paper because it is technically complicated.
Original fragment:
<<The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
>> summary:
The text is explaining the Transformer, a new type of AI language model. The Transformer is based solely on attention mechanisms, and is superior to existing models in quality while being more parallelizable and requiring less time to train.
<< input: Does it say why it is superior?
>> response:
Yes, the paper says that the Transformer model is superior because it is more parallelizable and requires less time to train.
"Superior because it is X" is not the same as "superior while also being X". GPT-3 has managed to say both of those things within your short session, only the latter actually being correct.
I've also been seing Astria, AvatarAI, (which apply Dreambooth to avatars) et al being launched recently, which is really wild. I think we'll see alot of builders applying ML to real UX.
Other interesting builder in the twitter maker space @miguelpiedrafita Which has done several AI related projects from a youtube video whisperer to srt subtitle to now building an AI script making tool, and also an auto-commit tool to make your commit messages for you.
I love to see the experimentation in the space, and now I'm thinking I need a bot to @savepapers which sends them to a readpaperlater which lets me read them/annotate in explainpaper ui
I was hoping it would magically translate some of the math notation into plain English but I think it kind of just ignored it. Would love to see this.
Minor feedback: the UI doesn't really convey that the highlighted text is being processed. I was wondering if anything was happening at first.
And we'll push an update soon with better loading :)
I signed up, but can't help wondering how long you plan to keep this a free service, without any obvious monetization of some kind?
Similar issues with people-sourced explanations I suppose!
I'm hoping that I substitute plain english words for lengthy patent jargon so that I can actually read the things for once.
But that's not all. If you pressure it into giving another joke, then it will give this anti-joke that appeared exactly once on Yahoo Answers many years ago. I don't remember the exact wording, but it's something like "A man went into a bar and ordered a beer. The bartender said, 'You're out of luck. We've been closed for fifteen minutes.'" When you search on Google for the joke (when you have the exact wording) you'll find that the Internet has been poisoned by this Yahoo Answer. I like to think that this is the joke.
Also, how expensive is this to run? I’d love to build some fun projects on GPT-3, but I haven’t dug into whether that’s cost prohibitive.
Looking at the network requests, it seems pricey to run though, as it appears to use a whole page as input.
I would pay for a more polished version of the service. This service is precious!
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