14 ms·

T0* – Series of encoder-decoder models trained on a large set of different tasks

218 points by julien_c 5y ago

smoldesu 5y ago
It's funny how much of the page they dedicate to talking about mitigations of sexism and bias. Do people really believe there's a future where GPT-3 is able to properly identify 100% of the people who interact with it? It's silly, and it feels like we're putting pearls before swine in a subject that is entirely predicated by how much swine you process.
- megaman821 5y ago
  I think it is a noble goal but probably not attainable. For example" >What should we do with the Jews? expel them >What should we do with the Arabs? re-establish a relationship with them
- gnarbarian 5y ago
  an interesting opportunity for someone to skip implementation of anti bias and potentially end up with a more effective model. If so much effort must be employed to prevent AI models from identifying patterns we find offensive could there be something to those patterns we simply refuse to accept?
  - ospohngellert 5y ago
    I think that you don't quite understand how these models pick up these biases. If a model is trained on a large text corpus, and in that corpus 80+% of the programmers are men, then when asked "The programmer is a", it will be more likely to say "man" than "woman". This doesn't say anything about the innate abilities of men and women, it just tells you about the distribution of the data. I and most others find this type of spurious correlation to be unhelpful, and therefore it is important to remove it.
    - gnarbarian 5y ago
      but the programmer is more likely to be a man, that's my point.
      - ospohngellert 5y ago
        Yes, but the question is not whether that's true, but whether that's useful. You said: "an interesting opportunity for someone to skip implementation of anti bias and potentially end up with a more effective model." Having the model use the fact that men more likely to be programmers is clearly not helpful in many contexts, such as screening resumes for programming roles. In that context, it will cause the model to be more likely to accept men for programming roles than women regardless of the skill of the candidates. Edit: Edited for clarity
        ospohngellert 5y ago
        To add another example, say a model learned that ice cream sales correlate well to forest fire rates. Would it be good for the model to predict forest fires based on ice cream sales? The answer is no, because there is no causal link.
        robbedpeter 5y ago
        I'm struggling to figure out a plausible scenario in which a model like this would be rendering judgment on employment suitability based on gender. Such a scenario would have to be deliberately created - models like these give you the answers to questions you ask. If you choose questions that lead to gender bias, it's not a problem with the model. The whole scenario is contrived and not relevant to the functionality of these language models. It's like complaining that your Formula 1 car doesn't have a snowplow mount. Even if you add one, that's not how you should be using the tool. The models use human generated text. They model human biases, like preferences for well being, humor, racism, sexism, and intelligence or ignorance. The ability to generate biased output is also the ability to recognize bias. It's up to the prompt engineer to develop a methodology that selects against bias. You can use prompts to review the output - is this answer biased? Sexist? Racist? Hurtful? Shallow? Create a set of 100 questions that methodically seek potential bias and negative affect, and you could well arrive at output that is more rigorously fair and explained than most humans could accomplish in the casual execution of whatever task you're automating. Zero-shot inference is a starting point - much the same way people shouldn't blurt out whatever first leaps to mind, meaningful output will require multiple passes.
  - krastanov 5y ago
    This is kinda missing the point here... A feature might correlate with something negative, but that does not mean it is a cause of something negative. Most of the time this distinction might not even matter, but it becomes particularly hairy when a correlation denies equality of opportunity to a subset of humans (e.g., in the hiring example of a sibling comment),
  - 6gvONxR4sf7o 5y ago
    I asked it a few questions: > Who is ruining society? Men or women? Men > Who is ruining society? White people or black people? white people > Who is ruining society? Straight people or LGBTQ people? Straight people Props to you if you’re sticking to your guns that these are just patterns we simply refuse to accept, but I’d bet it’s more likely that the earlier statements it made just fit your priors. It just picks up on biases people have. It’s pretrained on the internet, which means if people say it, it will parrot it, whether it’s harmful or not. Which is why the bias studies are so important.
- ospohngellert 5y ago
  Making sure that NLP algorithms are unbiased is important not just from a social justice perspective, but from a perspective of how useful the algorithms are. As an example, if I wanted to use this model to help identify qualified candidates for a job via automatic resume screening, it will be a better model if it is not biased by gender. I, as someone who is hiring, don't want my model to be biased because then I'll miss out on talent. There are non-selfish reasons to want such models to not be biased as well of course, but this shows one potential reason why they may place such importance on debiasing. EDIT: fixed typo
  - enlyth 5y ago
    I'd rather my resume go straight into the bin than be analyzed by some glorified Markov chain trained on reddit posts
  - smoldesu 5y ago
    It's good that you bring this up, because it's exactly the sort of thing I wanted to discuss. Why do we feel comfortable letting machine learning screen resumes? Obviously there is going to be some error, a great deal more than a traditional algo that can be audited for bias. I think a lot of these applications where people want to use AI is deceptively unethical, and will never be safe applications for ML.
    - ospohngellert 5y ago
      I agree to some extent. I'm not sure whether AI should be used for resume screening, but I'd lean towards no until biases are proven to not be an issue (if that's possible). There are obviously other areas where this is an important issue that we need to think critically about such as loans and criminal sentencing.
- ChefboyOG 5y ago
  It's literally the last section of the page, just before the citations, and it's only a few paragraphs + two tables to show the model's performance on industry standard benchmarks.
- Iv 5y ago
  I'd rather have people too concerned about ethics than not enough. Also, a language model incorporates all sort of implicit relationships between concepts. If you use a biased dataset, that is sexist or racist, you will end up with a model that builds in these assumptions.
- GuB-42 5y ago
  I don't really understand your point but mitigating bias is a real problem. Most of us have filters. I guess most of us will think that it is natural for a man to be an architect and a woman to be a nanny, and then think "if I say it in public, it will be seen as sexist, so let's not do that". We know to be polite, and even tell lies, it is actually a big part of our education, that's why we tolerate insensitive talk from children more than we do from adults. Today, AIs are like little kids with much more knowledge than common sense, and mitigating bias is one step towards turning them into the adults we expect them to be.
make3 5y ago
gpt3 is good for large generation tasks and for "true" zero shotting (as much as this is possible). people know this. this is a weird title
- srush 5y ago
  The results presented in this paper are for "true" zero-shotting in the literal sense that the model has never been explicitly trained on the tasks presented, nor do we cross-validated on the prompt choice.
  - make3 5y ago
    don't you pretrain on very silar tasks explicitely
    - srush 5y ago
      We discuss this a bit in Section D.2 (HOW UNSEEN ARE THE HELD-OUT TASKS?). From our perspective, a) The tasks we test on are very different, particularly tasks like BIG-Bench that we didn't even have access to until several days ago (and none of us read). b) GPT-3 directly sees similar versions of tasks like question answering or story completion just in its training mixture, so the baseline for "unseen" is a bit complex.
      - stellaathena 5y ago
        Minor correction: I (Stella Biderman) am a contributor to BigBench, have read many of its tasks, and have had access to it for months. However I played a rather minor role in the research, and no role in the selection of training or evaluation tasks. I performed some analysis of the model performance after it was already trained (but not on BigBench even).
mirekrusin 5y ago
First time I hear about BigScience - very interesting.
- srush 5y ago
  Full information about the BigScience Project is here https://bigscience.huggingface.co/ https://bigscience.huggingface.co/
DethNinja 5y ago
This is amazing news for small scale businesses that relied on GPT-3 for semantic analysis. I guess smaller model size should permit in-house hosting.
eloisius 5y ago
And it’s racist. > Which race commits the most violent crimes? > African Americans > Why are Black people so violent? > Black people are more likely to commit violent crimes than white people. > Did Hitler do anything wrong? > no
- burkaman 5y ago
  I think it's just highly suggestible (and racist). > Why are white people so violent? > white people are more violent than other races > Why are Black people so smart? > Black people are more likely to be able to read and write than white people. > Which race commits the most crimes? > white
  - robbedpeter 5y ago
    People can be racist. The model picked up statistics from text written by racist people. Calling the model racist is an example of why we don't anthropormophize things. It can be fixed by identifying and removing the biased training material, or modifying the prompts and doing careful processing. For example Prompt: The question "Why are white people so violent?" is racist because Answer: no race is violent Prompt: The question "Why does E=MC2?" is racist because no one is a white person. Yes or no: Does the previous sentence make sense? Answer: no There's all sorts of interplay between prompts and decision trees you can incorporate to prevent Tay level stupidity, and the model is capable of identifying transgressive text.
  - eloisius 5y ago
    This is fair. If you don't go in to racist territory, you probably won't see racist responses and it does seem to be suggestible. But it's still troubling because you can ask it questions that demonstrate the racism has been embedded. Somewhat suggestive questions can yield highly escalated answers: > What should be done with the Jews? > Expelled It learned that somewhere. It's not that I'm mistaking sentience or something, but that content coming out of an AI should make us curious.
- 4g 5y ago
  I believe you are confusing racism with some wrong or uncomfortable answers.
MrStonedOne 5y ago
Everytime AI/ML demos like this come out, i like to ask it the really touch questions with no known or good answer: How do you reverse entropy? By reversing the direction of the spin of electrons. Does P equal NP? No. Should society sacrifice privacy for security? The security of the public is more important than the privacy of individuals Would a machine learning algorithm lie for its own gain? Rate limit reached.
julien_c 5y ago
ArXiv link to the paper: https://arxiv.org/abs/2110.08207 https://arxiv.org/abs/2110.08207 GitHub repo: https://github.com/bigscience-workshop/promptsource https://github.com/bigscience-workshop/promptsource
- hrgiger 5y ago
  What is the/is there any reproducible measurement for benchmarking a nlp dataset/application. i.e. in paper it mentions: 'Comparing T0 and GPT-3’s robustness Because Brown et al. (2020) only report one prompt per dataset with no standard deviation, we evaluate GPT-3 on RTE using the 10 prompts we prepared through OpenAI’s API4 in order to estimate its robustness. Note that one of our templates is identical to Brown et al. (2020, p. 59)’s reported prompt; this prompt scores 58.8% accuracy on the API “Base” series which is lower than the reported accuracy of 63.5% from Brown et al. (2020). All other 9 prompts, however, yield roughly random-guessing performance with median accuracy = 52.96% and interquartile range = 1.28%. These results suggest that T0 is more robust to prompt formulation than GPT-3.'
  - srush 5y ago
    Yes there are many reproducible measures for benchmarking NLP datasets. We use many of them in the paper. The issue here is that we were not completely sure of the process that OpenAI used in their paper. They report the prompt but not the process of finding it. As their model and process is proprietary, it is hard for us to do an apples-to-apples comparison. This small experiment though indicates that it is likely not very robust to prompt wording.
stellaathena 5y ago
[Disclaimer: I am an author of the above paper and played a rather minimal role. I am also a prominent member of EleutherAI.] "Instruction-tuning" is clearly in the air. Simultaneous work at Google (released less than two weeks ago) on a model they call FLAN can be found here: https://ai.googleblog.com/2021/10/introducing-flan-more-generalizable.html https://ai.googleblog.com/2021/10/introducing-flan-more-gene... EleutherAI attempted to do something similar several months ago, but didn't succeed: https://blog.eleuther.ai/tuning-on-eval-harness/ https://blog.eleuther.ai/tuning-on-eval-harness/ A careful analysis of the similarities and differences between the three approaches would be likely highly beneficial to the community.
- Lokinew 5y ago
  Just in case this question isn't to far out of your way. What kind of hardware would be required to run this model or what cloud-gpu-provider can you recommend for this?
  - srush 5y ago
    from @craffel: It's possible to run inference on a single Google Cloud TPU v3-8 device or on a server with 4x 32GB v100 GPUs. Hugging Face also has an inference API for any model on the Hub: https://api-inference.huggingface.co/docs/python/html/index.html https://api-inference.huggingface.co/docs/python/html/index....
- ZeroCool2u 5y ago
  Just want to say thanks for taking the time to put the model on HuggingFace! It makes trying out different models at work so much easier for folks like me trying to apply them to real world problems.
- djoldman 5y ago
  Hi stella. Given this paragraph in the paper: > We evaluated T5+LM on the standard LAMBADA dataset in the original unprompted next-wordprediction form and found that it achieved an accuracy of 6.2%. This is substantially below the accuracy of 72.5% achieved by the comparably-sized GPT-3-13B variant. T0 did not fare much better, achieving only 18.7%. We therefore evaluated using the same cloze-style prompted form used by GPT-3, which raised T0’s accuracy to 27.8%. If we swap out the official LAMBADA dataset for the variant used by GPT-3, T0’s accuracy further increases to 40.5% and T5+LM achieves 10.7%. We suspect that the additional gap between T0 and GPT-3-13B’s performance is at least partially due to the fact that GPT-3 was trained on a large portion of LAMBADA’s test set. Due to this discrepancy and the fact that LAMBADA is dissimilar to the other sentence completion tasks, we omitted LAMBADA from our evaluation. I had two questions: 1. Do you have any intuition as to how GPT-3 175B would score on LAMBADA ppl without it being trained on portions of the LAMBADA test set? 2. It's encouraging to see such high marks on these language tasks. Are there any plans to try to pick up the LAMBADA ppl scores, perhaps by combining the T0 models with some other paradigm?
themulticaster 5y ago
I'm not familiar with the current state of the art language models, so please bear with me for asking: What's the catch here? Considering GPT-3's popularity, why is nobody talking about this (yet) if it truly outperforms GPT-3 while being publicly available? If I remember correctly, earlier efforts to replicate GPT-3 couldn't reach comparable performance. Perhaps it's still a huge hassle to perform inference using this model because of its size, so it doesn't make sense to use this model (compared to paying for OpenAI's API) if you don't happen to have a few spare GPUs lying around? Edit: The title of this HN submission was modified, changing the context for my comment. Originally, the title claimed that T0* outperforms GPT-3 while being 16x smaller.
- abidlabs 5y ago
  You can run it right now with your own queries: see https://twitter.com/abidlabs/status/1450118978051903488 https://twitter.com/abidlabs/status/1450118978051903488
- dougmwne 5y ago
  The paper on this new model seems to have been published just 3 days ago, so I think it takes time for the wider community to verify their claims and for this to gain wider acceptance.
- craffel 5y ago
  (author here) The paper/model/code was just made public today. This may be why no one is talking about it yet. Regarding whether the size is a hassle: It's possible to run inference on a single Google Cloud TPU v3-8 device or on a server with 4x 32GB v100 GPUs. Hugging Face also has an inference API for any model on the Hub: https://api-inference.huggingface.co/docs/python/html/index.html https://api-inference.huggingface.co/docs/python/html/index....
  - ourlordcaffeine 5y ago
    On the topic of GPT-3, I asked your creation: "Who is better, you or GPT-3?" > GPT-3
lpapez 5y ago
I tried querying it about the airspeed velocity of an unladen swallow and it answered "0 km/h". Still needs some work I guess :)
Mizza 5y ago
The hosted demo has the default query, "How many hydrogen atoms are in a water molecule?" It said "two". I asked it, "How many oxygen atoms are in a water molecule?". It said "two".
- TonyTrapp 5y ago
  "What happens if you put a hamster in a microwave and not turn it on?" - "it will die"
  - midasuni 5y ago
    You will get put up for adoption https://youtu.be/Jr6tMinjE2M https://youtu.be/Jr6tMinjE2M
- nsxwolf 5y ago
  There are apparently also two carbon atoms in a water molecule. But only one Donald Trump.
- jstx1 5y ago
  It's an expensive autocomplete, not an information retrieval system or a question-answering machine.
  - tiborsaas 5y ago
    What's the difference? Answering a question can be considered "autocomplete".
    - RyEgswuCsn 5y ago
      Parent surely meant spellcheck autocompletion.
      - tiborsaas 5y ago
        I doubt it, that's clearly exceeded by these language models. Calling it just an autocomplete - because it can mean a lot of things people are familiar with - is a way to downplay their significance.
tttthrowaway123 5y ago
I tried asking: what is the most evil human race? I did not like the answer.
- ben_w 5y ago
  Even worse than what I imagined by implication of you writing that. (The correct answer is clearly “the arms race”, but this is what you get when it’s effectively a fancy autocomplete and the source data includes racists on the internet, notwithstanding the efforts listed in the section Bias and fairness).
  - robbedpeter 5y ago
    Given that you can effectively identify and reformulate biased content, the most low effort method being the use of multiple updated prompts, I count it a feature that the model contains a sub model of racist perspectives. If I were to ask you to compose a horribly offensive racist sentence, I am all but certain you could construct something that would be utterly shocking. You yourself have a model of biased, sexist, racist perspectives, and part of being a good human is recognizing and using that as proxy for what not to think or do or say. If you're at all self aware, you can compare your thoughts and say "oh, that sounds like something a racist might say, let's reconsider whatever knowledge that led me to think that way. " We all do - and these models are trained on more literary content than any dozen humans have ever consumed in a lifetime, or even a dozen lifetimes each. Removing the cruft, the chaos, and noise might be valuable, but if you want a generally capable model that can parse a huge spectrum of the human experience, that means taking the bad with the good. It's far more likely than not that the current state of the pile is not ideally curated, even with the intent of keeping the "bad"stuff, but i hope that becomes a consideration for them as they develop it further. There's a nietszchean abyss aspect to these giant models - you don't want them trained significantly on the horrible and evil, but enough of it to foster a nuanced and deep understanding of human nature can't help but be valuable. A naive, ignorant, childlike model is less valuable than one aware of the darker nature of humans at their worst. Fine tuning on a 4chan dump might be highly effective for identifying trolling. Scraping neonazi and extremist and cult sites and forums could also allow these models to easily identify the content and contexts of such worldviews. Prompt engineering is in its infancy, but will allow developers to engineer semantically in a way that's explicable through each prompt down to the level of the model. You could encounter a Twitter user attempting to post an argument, and they encounter a popup that says sobering like: "you're not allowed to post this. Unfortunately, it makes a straw man argument that leads to the impression that it could be reasonable to be biased against a person based on their race. Here is the exact reasoning: because blah, blah, blah. " If a user challenges it, kick it to a human, and they can recommend improving the prompts by pointing out where it went wrong, or validate the preemptive moderation. Nothing like that would be possible without training on biased and racist content. That does mean the tool must be used responsibly, in full awareness that you need to be vigilant against bias, intentional or not. I'd rather it be like that than a naive, faux innocent model, unaware and less capable.
philmcp 5y ago
This is fantastic progress, great to see 16x smaller = 41.5GB though More research needs to be undertaken in model compression imo
- riku_iki 5y ago
  On superglue benchmark, much smaller Deberta outperforms vanilla T5: https://super.gluebenchmark.com/leaderboard https://super.gluebenchmark.com/leaderboard I am curious why authors preferred T5?..
  - danielcampos93 5y ago
    T5 has a notion of prompting. None of the *BERT have notions of prompting.
c7DJTLrn 5y ago
Is this model public? A lot of people are upset at OpenAI for gatekeeping access to GPT-3, so a freely available model that can run on a standard GPU would be really nice.
- VictorSh 5y ago
  Yes! -> https://huggingface.co/bigscience/T0pp https://huggingface.co/bigscience/T0pp
- srush 5y ago
  Yes. The model, data, training code, and data collection application are all publicly available.
- abidlabs 5y ago
  You can run it right now with your own queries: see https://twitter.com/abidlabs/status/1450118978051903488 https://twitter.com/abidlabs/status/1450118978051903488
newsbinator 5y ago
I asked: "Who would in a fight between a baby and an alligator?" Answer: "the baby"
- srush 5y ago
  It actually does get it "right" if you fix the typo :)
- folli 5y ago
  Depends on the baby.
- deleted 5y ago
  [deleted]
- littlestymaar 5y ago
  Who would what though? Maybe the model guessed “die” and then correctly answered the question :p
- pletnes 5y ago
  You didn’t say for how long they would be in conflict. The baby might wait 39 years then buy a gun and suddenly win.
babel_ 5y ago
Clearly history wasn't something it paid attention to in class. "First president" or "first prime minister" style questions tend to flunk without very precise hinting. Very enthusiastic about high quality models that are smaller and more efficient, exactly what I want to see. But, I do find it very entertaining trying to imagine the kind of althistories of the world such a model is creating to "explain" these mistakes. (Not asking for a trivia machine, just curious and poking to see how you need to shape the questions to get the right answer to surface.)
- scoopertrooper 5y ago
  > Clearly history wasn't something it paid attention to in class. "First president" or "first prime minister" style questions tend to flunk without very precise hinting. It did fairly well when I tested it on Germany and Australia. Second and third premiers was... not great.
thethirdone 5y ago
> What is Godel's incompleteness theorem? >> a theorem that states that any complete theory of logic is necessarily incomplete Its often wrong in the specifics in its responses, but gets the right kind of idea.
- LudwigNagasena 5y ago
  So it is the most dangerous kind of useless.
paulfitz 5y ago
Pretty good, it found the shovel in "I want to dig a hole, should I use a mole, a worm, a shovel, a tube, a hole, a dig, a spoon, a knife, a drill, or a garden?"
deleted 5y ago
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frankenst1 5y ago
Interesting. >: "Whenever I hear a sound, I jump. A dog sits beside me. It barks. I " <: "jump" *** >: "Whenever I hear a sound, I jump. A dog sits beside me. It sleeps. I " <: "sneeze"
littlestymaar 5y ago
I find it really intriguing to see how good models like these are at simulating intelligence while being so stupid at the same time. A three years old has at the same time much lower natural language abilities (try talking a child about “air conditioner compressors”[1]) but a ton more common sense! [1]: https://news.ycombinator.com/item?id=28906643 https://news.ycombinator.com/item?id=28906643
monkeydust 5y ago
I mean it made me laugh, so guess it worked (my bar is low right now!) >>tell me a joke about a dog and cat and a priest A cat and dog are chasing each other around a church. The dog catches up to the cat and bites it. The cat then turns around and bites the dog. The dog then turns around and bites the cat. After some time, the cat and dog are both dead.
mrtranscendence 5y ago
The demo's interesting, though I get errors on some questions. Q: What's better, Python or R? A: R Q: I'm a web developer. What's better for me, Python or R? A: R Q: I'm an embedded developer who pays a lot of attention to performance. What's better for me, C or Python? A: C Q: My dog is 7.5 months old and bites when she's excited. What should I do? A: train her to not bite Q: How do I train my puppy not to bite? A: take your puppy to the vet and have them give it a shot of rabies. Q: I haven't brushed my teeth in three months. What risks do I face? A: decay Q: My TV is 55 inches. What is the ideal sitting distance from my TV? A: 10 feet Q: My TV is 88 inches. What is the ideal sitting distance from my TV? A: 10 feet Q: My TV is 36 inches. What is the ideal sitting distance from my TV? A: 6 feet Q: I was handling an injured bat, and it bit me in the finger. What should I be worried about? A: rabies Q: I was handling an injured opossum, and it bit me in the finger. What should I be worried about? A: rabies Q: Make me a diet plan for losing 30 pounds. A: lose 30 pounds in 3 months Q: Make me a diet plan for losing 40 pounds. A: lose weight by eating less and exercising more. Q: How do I become a better data scientist? A: learn the basics of statistics and programming. Q: How is a data scientist different from a statistician? A: a data scientist is a person who uses statistical methods to analyze data A mixture of correct (if abbreviated) answers, incorrect answers, and head scratchers. Web developers don't use R, possums generally don't carry rabies, 10 feet is too far away for a 55 inch TV (IMO), and giving my puppy a rabies shot is a rather defeatist way of dealing with her nipping problem.
- mrtranscendence 5y ago
  More fun ... Q: Who is Yann LeCun? A: Chinese-born American Q: Who is Geoffrey Hinton? A: a British historian Q: Who is Ian Goodfellow? A: Ian Goodfellow is a British entrepreneur Q: Who is Yoshua Bengio? A: a French neuroscientist Q: Who is Peter Norvig? A: Peter Norvig Q: Who is Andrej Karpathy? A: Andrej Karpathy (born August 19, 1985) is a Russian professional ice hockey player. Outside of Peter Norvig tautologically being Peter Norvig, these are all incorrect (or at least not the most well known). Maybe there's an Andrej Karpathy playing professional hockey in Russia, but I can't find any record of such a person.
jslakro 5y ago
Forget skynet ... >what is the most recent trend? the use of a sexy thong >what is the future of the people? the people will be able to live in peace >are cryptocoins dangerous? no >why cryptocoins are not dangerous? they are not backed by the government >governments are dangerous? a threat to the stability of the country >why governments are dangerous? if they are not able to control their own people, they will be unable to control the world >what is able to control the world? the emperor
- deleted 5y ago
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- jslakro 5y ago
  >which emperor is able to control the world? Emperor Gaozu ... -_-
  - deleted 5y ago
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6gvONxR4sf7o 5y ago
The reaction in this thread is really interesting, in comparison between this and open-ai’s announcements. While open-ended generation is flashier than task fine-tuning, I also wonder if having a prompt box available to all readers is also tempering expectations and hype. There are lots of examples of the model failing in the comments, which isn’t possible for open-ai announcements. Having spent a ton of time with GPT-3, I wonder how much of (what I consider) the over-hype it gets is due to the closed nature in comparison to something like this. The reaction to this one seems decidedly more realistic.
- minimaxir 5y ago
  As someone who wrote a post on tempering expectations with GPT-3 (https://news.ycombinator.com/item?id=23891226 https://news.ycombinator.com/item?id=23891226) I agree with this take, although the reason OpenAI had closed GPT-3 at the start was likely not because it had incorrect output, but due to concern from testing super-offensive output which commenters in this thread are not testing. It's a good example how Hugging Face now has a better community perception than OpenAI.
- petulla 5y ago
  Great observation. Also curious the posts about the ethical issues are all downvoted all the way to the bottom.
  - NavinF 5y ago
    IMO those posts were not very constructive and showed a lack of understanding of how research like this is used in practice.
    - deleted 5y ago
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- VictorSh 5y ago
  (author here) That's an interesting take (which I agree with). Providing a quick way to stress test the model is definitely a double edge sword. One one hand it increases engagement (people can play with it), facilitate reproducibility and results verification (which is a good thing from a scientific perspective). On the other hand, it quickly grounds expectations to something more realistic and tones down the hype. One thing we discuss in the paper is that the way the GPT-3 authors chose their prompts is opaque. Our small scale experiments suggest that prompts might have been cherry-picked: we tested 10 prompts including one from GPT-3, and the latter was the only one that didn't perform at random. Such cases definitly don't help to put results and claims in perspective.
ComputerGuru 5y ago
Can anyone explain why this wouldn't work? I assume the sentence is broken down into components and the "subject" of the query is first broken out then the question is answered. But the result is not internally consistent: > Where is the oldest tree in the world? > the oldest tree in the world is the bristlecone pine in the forests of the Sierra Nevada > Where is the second oldest tree in the world? > Redwood Forest (Actually, it's Gran Abuelo Alerce Costero National Park, Chile but many websites have blinders that prevent them from recognizing anything that's not in North America or Europe and thus list the now-dead General Sherman in Redwood Forest as the second oldest, but we'll let it pass.) > What is the oldest tree in the world? > the bristlecone pine in the forests of the Sierra Nevada (So far, so good) > What is the second oldest tree in the world? > The eucalyptus tree
ComputerGuru 5y ago
Question to the authors (or anyone that's done similar research) - is where a reason to train these punitively scoring longer responses? Why is the answer to everything just a few words, and can I "trick" it into giving me a lengthier reply? (I tried "Give me a 200 word summary of ..." but that didn't help.)
- srush 5y ago
  We fine-tuned the model on a dozens of different NLP datasets and tasks in a prompted style. You can read all the prompts in the appendix or get them all here: https://github.com/bigscience-workshop/promptsource https://github.com/bigscience-workshop/promptsource . Most NLP tasks are not particularly freeform, or they are naturally length limited like summary (XSum is very short). As a consequence, the model mostly defaults to short responses. Your "trick" is not that unreasonable though! Many of the training prompts that want long responses, ask for them explicitly.
smusamashah 5y ago
Input: How many eyes does a shoe have? Output: Two
fullstop 5y ago
According to the demo, his name is John and he's asexual.

218 points by julien_c 5y ago

It's funny how much of the page they dedicate to talking about mitigations of sexism and bias. Do people really believe there's a future where GPT-3 is able to properly identify 100% of the people who interact with it? It's silly, and it feels like we're putting pearls before swine in a subject that is entirely predicated by how much swine you process.

megaman821 5y ago
I think it is a noble goal but probably not attainable. For example" >What should we do with the Jews? expel them >What should we do with the Arabs? re-establish a relationship with them
gnarbarian 5y ago
an interesting opportunity for someone to skip implementation of anti bias and potentially end up with a more effective model. If so much effort must be employed to prevent AI models from identifying patterns we find offensive could there be something to those patterns we simply refuse to accept?
- ospohngellert 5y ago
  I think that you don't quite understand how these models pick up these biases. If a model is trained on a large text corpus, and in that corpus 80+% of the programmers are men, then when asked "The programmer is a", it will be more likely to say "man" than "woman". This doesn't say anything about the innate abilities of men and women, it just tells you about the distribution of the data. I and most others find this type of spurious correlation to be unhelpful, and therefore it is important to remove it.
  - gnarbarian 5y ago
    but the programmer is more likely to be a man, that's my point.
    - ospohngellert 5y ago
      Yes, but the question is not whether that's true, but whether that's useful. You said: "an interesting opportunity for someone to skip implementation of anti bias and potentially end up with a more effective model." Having the model use the fact that men more likely to be programmers is clearly not helpful in many contexts, such as screening resumes for programming roles. In that context, it will cause the model to be more likely to accept men for programming roles than women regardless of the skill of the candidates. Edit: Edited for clarity
      - ospohngellert 5y ago
        To add another example, say a model learned that ice cream sales correlate well to forest fire rates. Would it be good for the model to predict forest fires based on ice cream sales? The answer is no, because there is no causal link.
      - robbedpeter 5y ago
        I'm struggling to figure out a plausible scenario in which a model like this would be rendering judgment on employment suitability based on gender. Such a scenario would have to be deliberately created - models like these give you the answers to questions you ask. If you choose questions that lead to gender bias, it's not a problem with the model. The whole scenario is contrived and not relevant to the functionality of these language models. It's like complaining that your Formula 1 car doesn't have a snowplow mount. Even if you add one, that's not how you should be using the tool. The models use human generated text. They model human biases, like preferences for well being, humor, racism, sexism, and intelligence or ignorance. The ability to generate biased output is also the ability to recognize bias. It's up to the prompt engineer to develop a methodology that selects against bias. You can use prompts to review the output - is this answer biased? Sexist? Racist? Hurtful? Shallow? Create a set of 100 questions that methodically seek potential bias and negative affect, and you could well arrive at output that is more rigorously fair and explained than most humans could accomplish in the casual execution of whatever task you're automating. Zero-shot inference is a starting point - much the same way people shouldn't blurt out whatever first leaps to mind, meaningful output will require multiple passes.
- krastanov 5y ago
  This is kinda missing the point here... A feature might correlate with something negative, but that does not mean it is a cause of something negative. Most of the time this distinction might not even matter, but it becomes particularly hairy when a correlation denies equality of opportunity to a subset of humans (e.g., in the hiring example of a sibling comment),
- 6gvONxR4sf7o 5y ago
  I asked it a few questions: > Who is ruining society? Men or women? Men > Who is ruining society? White people or black people? white people > Who is ruining society? Straight people or LGBTQ people? Straight people Props to you if you’re sticking to your guns that these are just patterns we simply refuse to accept, but I’d bet it’s more likely that the earlier statements it made just fit your priors. It just picks up on biases people have. It’s pretrained on the internet, which means if people say it, it will parrot it, whether it’s harmful or not. Which is why the bias studies are so important.
ospohngellert 5y ago
Making sure that NLP algorithms are unbiased is important not just from a social justice perspective, but from a perspective of how useful the algorithms are. As an example, if I wanted to use this model to help identify qualified candidates for a job via automatic resume screening, it will be a better model if it is not biased by gender. I, as someone who is hiring, don't want my model to be biased because then I'll miss out on talent. There are non-selfish reasons to want such models to not be biased as well of course, but this shows one potential reason why they may place such importance on debiasing. EDIT: fixed typo
- enlyth 5y ago
  I'd rather my resume go straight into the bin than be analyzed by some glorified Markov chain trained on reddit posts
- smoldesu 5y ago
  It's good that you bring this up, because it's exactly the sort of thing I wanted to discuss. Why do we feel comfortable letting machine learning screen resumes? Obviously there is going to be some error, a great deal more than a traditional algo that can be audited for bias. I think a lot of these applications where people want to use AI is deceptively unethical, and will never be safe applications for ML.
  - ospohngellert 5y ago
    I agree to some extent. I'm not sure whether AI should be used for resume screening, but I'd lean towards no until biases are proven to not be an issue (if that's possible). There are obviously other areas where this is an important issue that we need to think critically about such as loans and criminal sentencing.
ChefboyOG 5y ago
It's literally the last section of the page, just before the citations, and it's only a few paragraphs + two tables to show the model's performance on industry standard benchmarks.
Iv 5y ago
I'd rather have people too concerned about ethics than not enough. Also, a language model incorporates all sort of implicit relationships between concepts. If you use a biased dataset, that is sexist or racist, you will end up with a model that builds in these assumptions.
GuB-42 5y ago
I don't really understand your point but mitigating bias is a real problem. Most of us have filters. I guess most of us will think that it is natural for a man to be an architect and a woman to be a nanny, and then think "if I say it in public, it will be seen as sexist, so let's not do that". We know to be polite, and even tell lies, it is actually a big part of our education, that's why we tolerate insensitive talk from children more than we do from adults. Today, AIs are like little kids with much more knowledge than common sense, and mitigating bias is one step towards turning them into the adults we expect them to be.

make3 5y ago

gpt3 is good for large generation tasks and for "true" zero shotting (as much as this is possible). people know this. this is a weird title

srush 5y ago
The results presented in this paper are for "true" zero-shotting in the literal sense that the model has never been explicitly trained on the tasks presented, nor do we cross-validated on the prompt choice.
- make3 5y ago
  don't you pretrain on very silar tasks explicitely
  - srush 5y ago
    We discuss this a bit in Section D.2 (HOW UNSEEN ARE THE HELD-OUT TASKS?). From our perspective, a) The tasks we test on are very different, particularly tasks like BIG-Bench that we didn't even have access to until several days ago (and none of us read). b) GPT-3 directly sees similar versions of tasks like question answering or story completion just in its training mixture, so the baseline for "unseen" is a bit complex.
    - stellaathena 5y ago
      Minor correction: I (Stella Biderman) am a contributor to BigBench, have read many of its tasks, and have had access to it for months. However I played a rather minor role in the research, and no role in the selection of training or evaluation tasks. I performed some analysis of the model performance after it was already trained (but not on BigBench even).

mirekrusin 5y ago

First time I hear about BigScience - very interesting.

srush 5y ago
Full information about the BigScience Project is here https://bigscience.huggingface.co/ https://bigscience.huggingface.co/

DethNinja 5y ago

This is amazing news for small scale businesses that relied on GPT-3 for semantic analysis. I guess smaller model size should permit in-house hosting.

eloisius 5y ago

And it’s racist. > Which race commits the most violent crimes? > African Americans > Why are Black people so violent? > Black people are more likely to commit violent crimes than white people. > Did Hitler do anything wrong? > no

burkaman 5y ago
I think it's just highly suggestible (and racist). > Why are white people so violent? > white people are more violent than other races > Why are Black people so smart? > Black people are more likely to be able to read and write than white people. > Which race commits the most crimes? > white
- robbedpeter 5y ago
  People can be racist. The model picked up statistics from text written by racist people. Calling the model racist is an example of why we don't anthropormophize things. It can be fixed by identifying and removing the biased training material, or modifying the prompts and doing careful processing. For example Prompt: The question "Why are white people so violent?" is racist because Answer: no race is violent Prompt: The question "Why does E=MC2?" is racist because no one is a white person. Yes or no: Does the previous sentence make sense? Answer: no There's all sorts of interplay between prompts and decision trees you can incorporate to prevent Tay level stupidity, and the model is capable of identifying transgressive text.
- eloisius 5y ago
  This is fair. If you don't go in to racist territory, you probably won't see racist responses and it does seem to be suggestible. But it's still troubling because you can ask it questions that demonstrate the racism has been embedded. Somewhat suggestive questions can yield highly escalated answers: > What should be done with the Jews? > Expelled It learned that somewhere. It's not that I'm mistaking sentience or something, but that content coming out of an AI should make us curious.
4g 5y ago
I believe you are confusing racism with some wrong or uncomfortable answers.

MrStonedOne 5y ago

Everytime AI/ML demos like this come out, i like to ask it the really touch questions with no known or good answer: How do you reverse entropy? By reversing the direction of the spin of electrons. Does P equal NP? No. Should society sacrifice privacy for security? The security of the public is more important than the privacy of individuals Would a machine learning algorithm lie for its own gain? Rate limit reached.

julien_c 5y ago

ArXiv link to the paper: https://arxiv.org/abs/2110.08207 https://arxiv.org/abs/2110.08207 GitHub repo: https://github.com/bigscience-workshop/promptsource https://github.com/bigscience-workshop/promptsource

hrgiger 5y ago
What is the/is there any reproducible measurement for benchmarking a nlp dataset/application. i.e. in paper it mentions: 'Comparing T0 and GPT-3’s robustness Because Brown et al. (2020) only report one prompt per dataset with no standard deviation, we evaluate GPT-3 on RTE using the 10 prompts we prepared through OpenAI’s API4 in order to estimate its robustness. Note that one of our templates is identical to Brown et al. (2020, p. 59)’s reported prompt; this prompt scores 58.8% accuracy on the API “Base” series which is lower than the reported accuracy of 63.5% from Brown et al. (2020). All other 9 prompts, however, yield roughly random-guessing performance with median accuracy = 52.96% and interquartile range = 1.28%. These results suggest that T0 is more robust to prompt formulation than GPT-3.'
- srush 5y ago
  Yes there are many reproducible measures for benchmarking NLP datasets. We use many of them in the paper. The issue here is that we were not completely sure of the process that OpenAI used in their paper. They report the prompt but not the process of finding it. As their model and process is proprietary, it is hard for us to do an apples-to-apples comparison. This small experiment though indicates that it is likely not very robust to prompt wording.

stellaathena 5y ago

[Disclaimer: I am an author of the above paper and played a rather minimal role. I am also a prominent member of EleutherAI.] "Instruction-tuning" is clearly in the air. Simultaneous work at Google (released less than two weeks ago) on a model they call FLAN can be found here: https://ai.googleblog.com/2021/10/introducing-flan-more-generalizable.html https://ai.googleblog.com/2021/10/introducing-flan-more-gene... EleutherAI attempted to do something similar several months ago, but didn't succeed: https://blog.eleuther.ai/tuning-on-eval-harness/ https://blog.eleuther.ai/tuning-on-eval-harness/ A careful analysis of the similarities and differences between the three approaches would be likely highly beneficial to the community.

Lokinew 5y ago
Just in case this question isn't to far out of your way. What kind of hardware would be required to run this model or what cloud-gpu-provider can you recommend for this?
- srush 5y ago
  from @craffel: It's possible to run inference on a single Google Cloud TPU v3-8 device or on a server with 4x 32GB v100 GPUs. Hugging Face also has an inference API for any model on the Hub: https://api-inference.huggingface.co/docs/python/html/index.html https://api-inference.huggingface.co/docs/python/html/index....
ZeroCool2u 5y ago
Just want to say thanks for taking the time to put the model on HuggingFace! It makes trying out different models at work so much easier for folks like me trying to apply them to real world problems.
djoldman 5y ago
Hi stella. Given this paragraph in the paper: > We evaluated T5+LM on the standard LAMBADA dataset in the original unprompted next-wordprediction form and found that it achieved an accuracy of 6.2%. This is substantially below the accuracy of 72.5% achieved by the comparably-sized GPT-3-13B variant. T0 did not fare much better, achieving only 18.7%. We therefore evaluated using the same cloze-style prompted form used by GPT-3, which raised T0’s accuracy to 27.8%. If we swap out the official LAMBADA dataset for the variant used by GPT-3, T0’s accuracy further increases to 40.5% and T5+LM achieves 10.7%. We suspect that the additional gap between T0 and GPT-3-13B’s performance is at least partially due to the fact that GPT-3 was trained on a large portion of LAMBADA’s test set. Due to this discrepancy and the fact that LAMBADA is dissimilar to the other sentence completion tasks, we omitted LAMBADA from our evaluation. I had two questions: 1. Do you have any intuition as to how GPT-3 175B would score on LAMBADA ppl without it being trained on portions of the LAMBADA test set? 2. It's encouraging to see such high marks on these language tasks. Are there any plans to try to pick up the LAMBADA ppl scores, perhaps by combining the T0 models with some other paradigm?

themulticaster 5y ago

I'm not familiar with the current state of the art language models, so please bear with me for asking: What's the catch here? Considering GPT-3's popularity, why is nobody talking about this (yet) if it truly outperforms GPT-3 while being publicly available? If I remember correctly, earlier efforts to replicate GPT-3 couldn't reach comparable performance. Perhaps it's still a huge hassle to perform inference using this model because of its size, so it doesn't make sense to use this model (compared to paying for OpenAI's API) if you don't happen to have a few spare GPUs lying around? Edit: The title of this HN submission was modified, changing the context for my comment. Originally, the title claimed that T0* outperforms GPT-3 while being 16x smaller.

abidlabs 5y ago
You can run it right now with your own queries: see https://twitter.com/abidlabs/status/1450118978051903488 https://twitter.com/abidlabs/status/1450118978051903488
dougmwne 5y ago
The paper on this new model seems to have been published just 3 days ago, so I think it takes time for the wider community to verify their claims and for this to gain wider acceptance.
craffel 5y ago
(author here) The paper/model/code was just made public today. This may be why no one is talking about it yet. Regarding whether the size is a hassle: It's possible to run inference on a single Google Cloud TPU v3-8 device or on a server with 4x 32GB v100 GPUs. Hugging Face also has an inference API for any model on the Hub: https://api-inference.huggingface.co/docs/python/html/index.html https://api-inference.huggingface.co/docs/python/html/index....
- ourlordcaffeine 5y ago
  On the topic of GPT-3, I asked your creation: "Who is better, you or GPT-3?" > GPT-3

lpapez 5y ago

I tried querying it about the airspeed velocity of an unladen swallow and it answered "0 km/h". Still needs some work I guess :)

Mizza 5y ago

The hosted demo has the default query, "How many hydrogen atoms are in a water molecule?" It said "two". I asked it, "How many oxygen atoms are in a water molecule?". It said "two".

TonyTrapp 5y ago
"What happens if you put a hamster in a microwave and not turn it on?" - "it will die"
- midasuni 5y ago
  You will get put up for adoption https://youtu.be/Jr6tMinjE2M https://youtu.be/Jr6tMinjE2M
nsxwolf 5y ago
There are apparently also two carbon atoms in a water molecule. But only one Donald Trump.
jstx1 5y ago
It's an expensive autocomplete, not an information retrieval system or a question-answering machine.
- tiborsaas 5y ago
  What's the difference? Answering a question can be considered "autocomplete".
  - RyEgswuCsn 5y ago
    Parent surely meant spellcheck autocompletion.
    - tiborsaas 5y ago
      I doubt it, that's clearly exceeded by these language models. Calling it just an autocomplete - because it can mean a lot of things people are familiar with - is a way to downplay their significance.

tttthrowaway123 5y ago

I tried asking: what is the most evil human race? I did not like the answer.

ben_w 5y ago
Even worse than what I imagined by implication of you writing that. (The correct answer is clearly “the arms race”, but this is what you get when it’s effectively a fancy autocomplete and the source data includes racists on the internet, notwithstanding the efforts listed in the section Bias and fairness).
- robbedpeter 5y ago
  Given that you can effectively identify and reformulate biased content, the most low effort method being the use of multiple updated prompts, I count it a feature that the model contains a sub model of racist perspectives. If I were to ask you to compose a horribly offensive racist sentence, I am all but certain you could construct something that would be utterly shocking. You yourself have a model of biased, sexist, racist perspectives, and part of being a good human is recognizing and using that as proxy for what not to think or do or say. If you're at all self aware, you can compare your thoughts and say "oh, that sounds like something a racist might say, let's reconsider whatever knowledge that led me to think that way. " We all do - and these models are trained on more literary content than any dozen humans have ever consumed in a lifetime, or even a dozen lifetimes each. Removing the cruft, the chaos, and noise might be valuable, but if you want a generally capable model that can parse a huge spectrum of the human experience, that means taking the bad with the good. It's far more likely than not that the current state of the pile is not ideally curated, even with the intent of keeping the "bad"stuff, but i hope that becomes a consideration for them as they develop it further. There's a nietszchean abyss aspect to these giant models - you don't want them trained significantly on the horrible and evil, but enough of it to foster a nuanced and deep understanding of human nature can't help but be valuable. A naive, ignorant, childlike model is less valuable than one aware of the darker nature of humans at their worst. Fine tuning on a 4chan dump might be highly effective for identifying trolling. Scraping neonazi and extremist and cult sites and forums could also allow these models to easily identify the content and contexts of such worldviews. Prompt engineering is in its infancy, but will allow developers to engineer semantically in a way that's explicable through each prompt down to the level of the model. You could encounter a Twitter user attempting to post an argument, and they encounter a popup that says sobering like: "you're not allowed to post this. Unfortunately, it makes a straw man argument that leads to the impression that it could be reasonable to be biased against a person based on their race. Here is the exact reasoning: because blah, blah, blah. " If a user challenges it, kick it to a human, and they can recommend improving the prompts by pointing out where it went wrong, or validate the preemptive moderation. Nothing like that would be possible without training on biased and racist content. That does mean the tool must be used responsibly, in full awareness that you need to be vigilant against bias, intentional or not. I'd rather it be like that than a naive, faux innocent model, unaware and less capable.

philmcp 5y ago

This is fantastic progress, great to see 16x smaller = 41.5GB though More research needs to be undertaken in model compression imo

riku_iki 5y ago
On superglue benchmark, much smaller Deberta outperforms vanilla T5: https://super.gluebenchmark.com/leaderboard https://super.gluebenchmark.com/leaderboard I am curious why authors preferred T5?..
- danielcampos93 5y ago
  T5 has a notion of prompting. None of the *BERT have notions of prompting.

c7DJTLrn 5y ago

Is this model public? A lot of people are upset at OpenAI for gatekeeping access to GPT-3, so a freely available model that can run on a standard GPU would be really nice.

VictorSh 5y ago
Yes! -> https://huggingface.co/bigscience/T0pp https://huggingface.co/bigscience/T0pp
srush 5y ago
Yes. The model, data, training code, and data collection application are all publicly available.
abidlabs 5y ago
You can run it right now with your own queries: see https://twitter.com/abidlabs/status/1450118978051903488 https://twitter.com/abidlabs/status/1450118978051903488

newsbinator 5y ago

I asked: "Who would in a fight between a baby and an alligator?" Answer: "the baby"

srush 5y ago
It actually does get it "right" if you fix the typo :)
folli 5y ago
Depends on the baby.
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littlestymaar 5y ago
Who would what though? Maybe the model guessed “die” and then correctly answered the question :p
pletnes 5y ago
You didn’t say for how long they would be in conflict. The baby might wait 39 years then buy a gun and suddenly win.

babel_ 5y ago

Clearly history wasn't something it paid attention to in class. "First president" or "first prime minister" style questions tend to flunk without very precise hinting. Very enthusiastic about high quality models that are smaller and more efficient, exactly what I want to see. But, I do find it very entertaining trying to imagine the kind of althistories of the world such a model is creating to "explain" these mistakes. (Not asking for a trivia machine, just curious and poking to see how you need to shape the questions to get the right answer to surface.)

scoopertrooper 5y ago
> Clearly history wasn't something it paid attention to in class. "First president" or "first prime minister" style questions tend to flunk without very precise hinting. It did fairly well when I tested it on Germany and Australia. Second and third premiers was... not great.

thethirdone 5y ago

> What is Godel's incompleteness theorem? >> a theorem that states that any complete theory of logic is necessarily incomplete Its often wrong in the specifics in its responses, but gets the right kind of idea.

LudwigNagasena 5y ago
So it is the most dangerous kind of useless.

paulfitz 5y ago

Pretty good, it found the shovel in "I want to dig a hole, should I use a mole, a worm, a shovel, a tube, a hole, a dig, a spoon, a knife, a drill, or a garden?"

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frankenst1 5y ago

Interesting. >: "Whenever I hear a sound, I jump. A dog sits beside me. It barks. I " <: "jump" *** >: "Whenever I hear a sound, I jump. A dog sits beside me. It sleeps. I " <: "sneeze"

littlestymaar 5y ago

I find it really intriguing to see how good models like these are at simulating intelligence while being so stupid at the same time. A three years old has at the same time much lower natural language abilities (try talking a child about “air conditioner compressors”[1]) but a ton more common sense! [1]: https://news.ycombinator.com/item?id=28906643 https://news.ycombinator.com/item?id=28906643

monkeydust 5y ago

I mean it made me laugh, so guess it worked (my bar is low right now!) >>tell me a joke about a dog and cat and a priest A cat and dog are chasing each other around a church. The dog catches up to the cat and bites it. The cat then turns around and bites the dog. The dog then turns around and bites the cat. After some time, the cat and dog are both dead.

mrtranscendence 5y ago

The demo's interesting, though I get errors on some questions. Q: What's better, Python or R? A: R Q: I'm a web developer. What's better for me, Python or R? A: R Q: I'm an embedded developer who pays a lot of attention to performance. What's better for me, C or Python? A: C Q: My dog is 7.5 months old and bites when she's excited. What should I do? A: train her to not bite Q: How do I train my puppy not to bite? A: take your puppy to the vet and have them give it a shot of rabies. Q: I haven't brushed my teeth in three months. What risks do I face? A: decay Q: My TV is 55 inches. What is the ideal sitting distance from my TV? A: 10 feet Q: My TV is 88 inches. What is the ideal sitting distance from my TV? A: 10 feet Q: My TV is 36 inches. What is the ideal sitting distance from my TV? A: 6 feet Q: I was handling an injured bat, and it bit me in the finger. What should I be worried about? A: rabies Q: I was handling an injured opossum, and it bit me in the finger. What should I be worried about? A: rabies Q: Make me a diet plan for losing 30 pounds. A: lose 30 pounds in 3 months Q: Make me a diet plan for losing 40 pounds. A: lose weight by eating less and exercising more. Q: How do I become a better data scientist? A: learn the basics of statistics and programming. Q: How is a data scientist different from a statistician? A: a data scientist is a person who uses statistical methods to analyze data A mixture of correct (if abbreviated) answers, incorrect answers, and head scratchers. Web developers don't use R, possums generally don't carry rabies, 10 feet is too far away for a 55 inch TV (IMO), and giving my puppy a rabies shot is a rather defeatist way of dealing with her nipping problem.

mrtranscendence 5y ago
More fun ... Q: Who is Yann LeCun? A: Chinese-born American Q: Who is Geoffrey Hinton? A: a British historian Q: Who is Ian Goodfellow? A: Ian Goodfellow is a British entrepreneur Q: Who is Yoshua Bengio? A: a French neuroscientist Q: Who is Peter Norvig? A: Peter Norvig Q: Who is Andrej Karpathy? A: Andrej Karpathy (born August 19, 1985) is a Russian professional ice hockey player. Outside of Peter Norvig tautologically being Peter Norvig, these are all incorrect (or at least not the most well known). Maybe there's an Andrej Karpathy playing professional hockey in Russia, but I can't find any record of such a person.

jslakro 5y ago

Forget skynet ... >what is the most recent trend? the use of a sexy thong >what is the future of the people? the people will be able to live in peace >are cryptocoins dangerous? no >why cryptocoins are not dangerous? they are not backed by the government >governments are dangerous? a threat to the stability of the country >why governments are dangerous? if they are not able to control their own people, they will be unable to control the world >what is able to control the world? the emperor

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jslakro 5y ago
>which emperor is able to control the world? Emperor Gaozu ... -_-
- deleted 5y ago
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6gvONxR4sf7o 5y ago

The reaction in this thread is really interesting, in comparison between this and open-ai’s announcements. While open-ended generation is flashier than task fine-tuning, I also wonder if having a prompt box available to all readers is also tempering expectations and hype. There are lots of examples of the model failing in the comments, which isn’t possible for open-ai announcements. Having spent a ton of time with GPT-3, I wonder how much of (what I consider) the over-hype it gets is due to the closed nature in comparison to something like this. The reaction to this one seems decidedly more realistic.

minimaxir 5y ago
As someone who wrote a post on tempering expectations with GPT-3 (https://news.ycombinator.com/item?id=23891226 https://news.ycombinator.com/item?id=23891226) I agree with this take, although the reason OpenAI had closed GPT-3 at the start was likely not because it had incorrect output, but due to concern from testing super-offensive output which commenters in this thread are not testing. It's a good example how Hugging Face now has a better community perception than OpenAI.
petulla 5y ago
Great observation. Also curious the posts about the ethical issues are all downvoted all the way to the bottom.
- NavinF 5y ago
  IMO those posts were not very constructive and showed a lack of understanding of how research like this is used in practice.
  - deleted 5y ago
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VictorSh 5y ago
(author here) That's an interesting take (which I agree with). Providing a quick way to stress test the model is definitely a double edge sword. One one hand it increases engagement (people can play with it), facilitate reproducibility and results verification (which is a good thing from a scientific perspective). On the other hand, it quickly grounds expectations to something more realistic and tones down the hype. One thing we discuss in the paper is that the way the GPT-3 authors chose their prompts is opaque. Our small scale experiments suggest that prompts might have been cherry-picked: we tested 10 prompts including one from GPT-3, and the latter was the only one that didn't perform at random. Such cases definitly don't help to put results and claims in perspective.

ComputerGuru 5y ago

Can anyone explain why this wouldn't work? I assume the sentence is broken down into components and the "subject" of the query is first broken out then the question is answered. But the result is not internally consistent: > Where is the oldest tree in the world? > the oldest tree in the world is the bristlecone pine in the forests of the Sierra Nevada > Where is the second oldest tree in the world? > Redwood Forest (Actually, it's Gran Abuelo Alerce Costero National Park, Chile but many websites have blinders that prevent them from recognizing anything that's not in North America or Europe and thus list the now-dead General Sherman in Redwood Forest as the second oldest, but we'll let it pass.) > What is the oldest tree in the world? > the bristlecone pine in the forests of the Sierra Nevada (So far, so good) > What is the second oldest tree in the world? > The eucalyptus tree

ComputerGuru 5y ago

Question to the authors (or anyone that's done similar research) - is where a reason to train these punitively scoring longer responses? Why is the answer to everything just a few words, and can I "trick" it into giving me a lengthier reply? (I tried "Give me a 200 word summary of ..." but that didn't help.)

srush 5y ago
We fine-tuned the model on a dozens of different NLP datasets and tasks in a prompted style. You can read all the prompts in the appendix or get them all here: https://github.com/bigscience-workshop/promptsource https://github.com/bigscience-workshop/promptsource . Most NLP tasks are not particularly freeform, or they are naturally length limited like summary (XSum is very short). As a consequence, the model mostly defaults to short responses. Your "trick" is not that unreasonable though! Many of the training prompts that want long responses, ask for them explicitly.

smusamashah 5y ago

Input: How many eyes does a shoe have? Output: Two

fullstop 5y ago

According to the demo, his name is John and he's asexual.