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GPT-3 vs. 3M free-text trivia responses
- minimaxir 6y ago> Credit where due: friend of WCT Dennis wrote the script to feed Water Cooler Trivia questions to GPT-3 and access the OpenAI API. It should be noted it's against OpenAI's rules to share access to GPT-3, although they've been inconsistent about enforcing it.
- agravier 6y ago> 2. Clues confuse GPT-3. They should probably have been removed. This gives me the overall impression that the testers treat GPT-3 a bit too much as something like an artificial human, and not enough like an algorithm (which will work better with sanitized input). This is not a major criticism, the experiment is still interesting. Could it be that the marketing from OpenAI it to blame? From the OpenAI front page: > Discovering and enacting the path to safe artificial general intelligence. > Our first-of-its-kind API can be applied to any language task, and currently serves millions of production requests each day. Does that seem misleading?
- cowllin 6y agoI thought about removing those two-word clues but ultimately, they exist for users so I wanted to be closer to apples-to-apples!
- minimaxir 6y agoOpenAI's marketing has been fair, and it's not misleading (GPT-3 can be applied to any language tasks but your mileage will vary as this submission demonstrates). However, OpenAI's endorsement of the demos amplifying and anthropomorphizing GPT-3 as a sentient mind don't help, and it's been disappointing that OpenAI doesn't really push back on that. (the hype is what prompted my rebuttal on GPT-3 expectations, which still holds up: https://news.ycombinator.com/item?id=23891226 https://news.ycombinator.com/item?id=23891226) I do believe the future of AI text generation is more bespoke, algorithmic-input-friendly models which is what I've been working on as my side project.
- 2sk21 6y agoVery good write-up. It seems too soon to think of genuine commercialization at this time.
- wyldfire 6y ago"Two'fer Goofer", "Tough Training" - why would GPT-3 give the question back as its response in these cases?
- fpgaminer 6y agoGPT-3 has a habit of repeating its input. GPT-2 had the same problem, as did all language models before. LSTMs were really bad in this respect and would often get stuck in single word loops. GPT-3 just does it less often, and tends to repeat sentences or ideas, rather than single words. A larger corpus and more training seemed to have helped reduce the issue, and I think they tweaked the temperature algorithms as well (I think top-p is an option now, whereas most language models used top-k historically). In my experience GPT repeats itself more often when it doesn't understand the prompt. I mean, if you don't understand _anything_ about an input sequence, what is the most likely next word? One of the previously used words. It's likely that the following occurs: Given the phrase "1+1=? ", the first layer assigns a baseline probability to "1", "+", "=", and "?", because those occur in the input and its a good fallback. But then the layers after recognize the input as a math equation and quickly assign a high probability to "2". Since the probability on "2" is so high the temperature algorithm always picks "2" as the output token. But if we imagine GPT doesn't understand math, then all the layers after the first have no clue what the input is. They can't assign higher probability to anything. They might make a few weak guesses, say "duck". But it isn't confident in them, so really "duck" is as likely as "1". The temperature algorithm then has a fairly decent chance of selecting "1" as the output token. Now GPT is given "1+1=? 1" and it seems like "+" is the next logical token. "1+1=? 1+". And so forth until it GPT is stuck in self reinforcing a loop saying "1+1=? 1+1=? 1+1=? 1+1=?" etc. Funny thing is, it's not like humans don't do this. When you get asked a hard question that you don't have an answer for, don't you find yourself repeating the question to yourself over and over again, hoping you'll better understand it? I'm not necessarily creating a parallel between that behavior and GPT's behavior. Just food for thought, I suppose.
- aabhay 6y agoGiven the parallel you draw at the end, perhaps future models will adopt some kind of common phrase to denote thinking, such as ‘Umm’, ‘er’, etc. Maybe there’s even a neurologically concise explanation for why humans do that!
- darepublic 6y agoHow is GPT3 considered publicly available? I applied months ago and have received no response.
- fpgaminer 6y agoSomewhat related, but I can't stop thinking about what's going to happen when GPT-4 reads articles like this. Articles like these that discuss GPT-3's output, or comments and articles directly generated by GPT-3, are going to be fed into GPT-4 as part of a theoretical WebText3 dataset. Will that help or hurt GPT-4's learning? What effect will it have? Theoretically you can feed GPT-3's responses back into itself and ask it to introspect about them; whether it was right or wrong and to comment on why it gave the response it did. But I doubt GPT-3 is particularly good at self introspection. GPT-3 was trained before GPT-3 existed, obviously. So it was never trained on articles analyzing the output of an AI of its caliber. But GPT-4 is going to be trained on a corpus filled with people analyzing GPT-3's outputs, like this article. We would expect GPT-4 to be able to write an article like this. So it should be theoretically possible to give GPT-4 its own output, and then ask it to provide introspection, and for that introspection to be insightful. EDIT: Follow up thought. It's almost as if the internet is being filled with a training corpus on GPT-3's failings. Every fact that GPT-3 failed to learn from WebText2 is now going to be repeated, alongside the correct answer, in WebText3. Humans are globally working, unknowingly, to build a curated dataset by which GPT-4 can learn from GPT-3's mistakes.
- mrfusion 6y agoSomewhere in it’s billions of parameters it will have a sense of self. A model of itself. That’s pretty mind boggling.
- bjourne 6y agoAll text generated by current language models is defective. For reasons that are not fully understood, text language models emit is less interesting than text emitted by humans. Unlike humans, they can not score well on both coherence, as in grammatically correct sentences, and interest, as in "having something to say". They are however good at "coherent rambling". Since most language models work by using its outputs as its inputs, this means that the emitted text becomes worse the more words that are emitted. Suppose you want your model to emit a novel. The first 30 words might come out ok. The next 30 are so-so. Eventually, after a few hundred words, the model will begin to ramble and will get stuck in loops: "then she went to visit her mother and she went to visit her mother and she vent to visit her mother and she ..." All language models suffer from this problem; Markov models, RNNs, Transformers, GPT-2/3... In fact, one could argue that GPT-3 has "only" decreased the deterioration speed. Markov models deteriorate after a dozen words while GPT-3 can keep going for perhaps a hundred words before it begins to ramble. However, the statistical properties of the text GPT-3 generates are different from what a human emits (and what those "statistical properties" are is unknown - it appears that capturing what makes human prose human-like in a mathematical formula is very difficult) from the very first word so you can't use GPT-3-generated text to train GPT-4. The paper "The Curious Case of Neural Text Degeneration" describes the problem in more detail and with math. A major breakthrough in language modeling may come if someone is able to invent a model that doesn't degenerate. One whose thousandth word is as human-like as its first word.
- gwern 6y ago"The robot was best at Fine Arts and Current Events, worst at Word Play and Social Studies. ...This one’s not so surprising. We have a type of question called a “Two’fer Goofer” which asks for a pair of rhyming words that satisfy a given clue. It’s similar to the Rhyme Time category in Jeopardy or the old newspaper puzzle Wordy Gurdy. We had three of these questions in the showdown and GPT-3 missed all three of them. For Word Play questions that were more like vocabulary quizzes, GPT-3 performed admirably:" More evidence, if it was needed, that the use of BPEs sabotages GPT-3 in a lot of subtle ways (https://www.gwern.net/GPT-3#bpes https://www.gwern.net/GPT-3#bpes). GPT-3 can understand things like vocab definitions which do not depend on the internal spelling of a word or phonetics (which are erased by the BPE encoding of the data it was trained on), but as soon as you have to do things like puns... The BPEs are deadly. Ah well. Eventually OA or someone will train a proper character-level model, and then I'll be able to generate rhyming poetry without hacks like rhyming dictionaries.
- hakuseki 6y ago> The more important takeaway: dozens of technologists at IBM spent more than three years and untold millions of dollars building the program specifically trained for Jeopardy! prowess. Less than 10 years later, a general-purpose open-sourced technology without the massive mainframe or cooling fans can compete on the same level. This statement from the article confused me. GPT-3 is general-purpose, but not open source, nor does it run without massive hardware, nor was it developed without spending millions of dollars.
- atleta 6y agoYep, it's not only open sourced, it's not even open access. (I get the reason, but somehow, maybe due to the fact that it's from an organization called OpenAI, a lot of people seem to think that everyone could use it if they wanted to.)
- gwern 6y agoYou could compare it to T5 instead. T5 does awesome at trivia, both the code and trained models are available, and it'll run on ordinary hardware. (Not sure if one could argue 'the millions of dollars' part, but that GB R&D wasn't spent for the purpose of trivia playing like the Watson Jeopardy system was aimed at doing, so seems reasonably fair to me - the trivia playing is merely the byproduct of the target capabilities of translation, knowledge graph construction etc).
- blueblisters 6y agoCould GPT-3 lower barriers of entry for search engines? Assuming, it could update its "index" (weights) at regular intervals, it's not too far-fetched to see it competing with Google with high-quality relevant answers to queries.
- ronsor 6y agoI've tried asking GPT-3 factual questions and sometimes it does great. Other times, well... let's just say it doesn't quite understand. Bad answers are in italics. Q: How tall is the Eiffel Tower? A: About twenty feet. Q: What color are the clouds? A: They're a light blue. Q: Which way does the sun rise in Paris? A: You'd have to be standing on top of a building to see it because it doesn't rise that high. Q: Who invented the telephone? A: Alexander Graham Bell. Q: Who invented Google? A: Larry Page and Sergey Brin. Q: What's the capital of California? A: It's not located in California. It's Sacramento.
- ctl0 6y agoThere are some works resonate with your idea, here's the first one I can recall. https://arxiv.org/abs/2012.14983 https://arxiv.org/abs/2012.14983
- dumbsqr 6y agoI think than human communication is better than any ai model because human goes from general to concrete, adding more information to convert general context into insightful ideas. We use language to communicate what is important for us. Ai is about navigating in an ocean trying not to be captured by concrete things, because it knows nothing about being real. As a mathematical methafor, each word is a window in the tangent space of an n-dimensional sphere and the normal points to the center of the sphere, as time goes on communication is about shrinking the radious of the sphere so that the center is like the kernel of what we wish to communicate. The sphere in an ai system has a very big radius and is not able to shrink because it lacks a kernel. You could simulate a human conversation with an ai, for example to sell something following steps like description of a product, price advantages and so on, but in a real context those are ad hoc methods and there is no general rule to shrink that is to penetrate deep into the meaning of what you want to communicate.
- johbjo 6y ago> Less than 10 years later, a general-purpose open-sourced technology without the massive mainframe or cooling fans can compete on the same level. First, are GPT-3 models generally available? Secondly, GPT-3 is many orders of magnitude larger than any model existing 10 years ago. There are certainly cooling fans involved.
- vessenes 6y agoIt would be helpful to see the prompt structure used for gpt-3: state of the art prompting methods do significantly better than naive ones, and randomness and other settings can make a big difference in quality as well. There are also cyclical prompt methods which can help derandomize gpt3, for example, you could generate three to five answers per question then feed the set back to gpt-3 and ask it which is the most correct; the first round would use high randomness settings the last very low. My own experience is that you sort of have to work with gpt3 to get on the same page sometimes, and when you do the results can be remarkable. Anyway, fun idea!
- kashyapc 6y agoThere was recently a thread[1] about an open-source alternative to GPT-3 here, called "GPT-Neo". I hope it takes off. But the language on their website undermines some confidence: "GPT-Neo is the code name for a series of transformer-based language models [...] we plan to train and open source." PS: The original URL that was submitted to HN is now 404; it moved to here[2]. [1] https://news.ycombinator.com/item?id=25819803 https://news.ycombinator.com/item?id=25819803 [2] https://www.eleuther.ai/projects/gpt-neo/ https://www.eleuther.ai/projects/gpt-neo/