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OPT: Open Pre-trained Transformer Language Models

arxiv.org

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Re: OPT: Open Pre-trained Transformer Language Models

#61

Post by dukeofdoom »

astrange wrote: post #29
dukeofdoom wrote: post #14

Earlier quoted context omitted.

To prevent someone from building something that returns certain inferences that might be true but are politically taboo.

GPT3 will do that right now. There aren’t any controls on its text, it just warns you if it looks offensive. And of course nothing it says is true except coincidentally. If you've seen GPT-3 interviews ( https://twitter.com/minimaxir/status/1513957106868637696 ) it'll happily say some wild stuff. As a mild example I recommend interviewing "a man who is currently beating you up".

Is it true to say they are true coincidentally, because that kind of suggests randomly true. I understand the AI doesn't really comprehend if something is true or false. My understanding is the results are more than random, maybe something closer to like weighted opinion.

Re: OPT: Open Pre-trained Transformer Language Models

#62

Post by yosito »

thorum wrote: post #31

A quick summary of the Limitations section: - "OPT-175B does not work well with declarative instructions or point-blank interrogatives." - "OPT-175B also tends to be repetitive and can easily get stuck in a loop. While sampling can reduce the incidence rate of repetitive behavior (Holtzman et al., 2020), we anecdotally found it did not eliminate it entirely when only one generation is sampled." - "We also find OPT-17…

> OPT-175B has a high propensity to generate toxic language and reinforce harmful stereotypes

So they trained it on Facebook comments?

Re: OPT: Open Pre-trained Transformer Language Models

#63

Post by mwint »

mikolajw wrote: post #42

The big one, OPT-175B, isn't an open model. The word "open" in technology means that everyone has equal access (viz. "open source software" and "open source hardware"). The article says that research access will be provided upon request for "academic researchers; those affiliated with organizations in government, civil society, and academia; and those in industry research laboratories.". Don't assume any good intent…

Add Mathematica to that list, too. Pretty cool to play with and I would have bought a license if I had a good excuse to; the tactic works.

Re: OPT: Open Pre-trained Transformer Language Models

#64

Post by thrtythreeforty »

PeterisP wrote: post #39
faebi wrote: post #26

Earlier quoted context omitted.

Does the model need to be in memory in order to run it with current tooling?

To run it at a reasonable speed, yes. Computing a single word requires all of the parameters; if you don't have them in memory you'd have to re-transfer all those gigabytes to the GPU for each full pass to get some output, which is a severe performance hit as you can't fully use your compute power because the bandwidth is likely to be the bottleneck - running inference for just a single example will take many seconds…

Exactly! This is called "model parallelism" - each layer of the graph is spread across multiple compute devices. Large clusters like the V100s or the forthcoming trn1 instances (disclosure, I work on this team) need _stupid_ amounts of inter-device bandwidth, particularly for training.

Re: OPT: Open Pre-trained Transformer Language Models

#65

Post by daenz »

voz_ wrote: post #60
daenz wrote: post #57

Earlier quoted context omitted.

>You cannot fingerprint models like this A GAN can absolutely be trained to discriminate between text generated from this model or another model. >that's hilarious What's hilarious about it?

> A GAN can absolutely be trained to discriminate between text generated from this model or another model. Nope. I dare you to do it. Or at least intelligently articulate the model architectures for doing so. > What's hilarious about it? It's a bullshit term, firstoff, and calling yourself that is the height of ego. Might as well throw in rockstar, ninja, etc too.

So in the entire field of machine learning, we can't train a model that can identify another model from its output? Just can't be done? And there's absolutely no value in having tools that can identify deep fakes, or content produced by specific open models?

>It's a bullshit term, firstoff, and calling yourself that is the height of ego

I am a 10x engineer though, so I'm sorry if that rubs you the wrong way. Also, you're reading my personal website, so of course I'm going to speak highly of myself :)

Re: OPT: Open Pre-trained Transformer Language Models

#66

Post by astrange »

daenz wrote: post #57
voz_ wrote: post #55

Earlier quoted context omitted.

> I will never discuss FB technical details, internals, or anything else on this site, so please do not ask. My claim of nonsense has nothing to do with FB. You cannot fingerprint models like this, that's just not how it works. Also, if we are reading profiles, you call yourself a 10x engineer on your blog, that's hilarious. Maybe 10x the nonsense?

>You cannot fingerprint models like this A GAN can absolutely be trained to discriminate between text generated from this model or another model. >that's hilarious What's hilarious about it?

That would be interesting if it was true, but I think it can’t be true because LLMs main advantage is they memorize text in their weights and so your discriminator model would need to be the same size as the LLM.

That said the smaller GPT3 models break down quite often so they’re probably detectable.

Re: OPT: Open Pre-trained Transformer Language Models

#67

Post by CrispinS »

dukeofdoom wrote: post #47
CrispinS wrote: post #44

Earlier quoted context omitted.

> The second link returned on him was from ADL. No way that's an organic result. It might be, actually. I understand why you'd think that, but look at the results for other search engines. Kagi: ADL in 2nd place Bing: ADL in 3rd place Yandex: ADL not on the first page, but SPLC[1] is the the 6th result [1]: https://www.splcenter.org/fighting-hate/extremist-files/indi...

This logic kind of fails quickly. I bet you wouldn't use it to show that Tiananmen Square did not happen, by showing all Chinese Search Engine are in apparent agreement on it not happening.

Well, no, which is why I threw in Kagi and Yandex as well. I can imagine Google and Microsoft altering rankings for certain results for political reasons, but Kagi seems too small to care about that, and Yandex isn't operating from the same political playbook as western corporations.

Now, in defense of your theory, I did double check Kagi and found out that they use Bing and Google for some queries, so the only truly "untainted" one is Yandex, which doesn't have ADL on the first page, or the next five that I checked.

That said, as I mentioned they do surface SPLC, which is similar in tone and content.

Limited sample size, but I think it's still plausible that ADL is an organic result.

I also checked Yahoo, and it has ADL as the third result.

I checked Baidu and Naver, and didn't see ADL, but I assume they're prioritizing regional content.

Re: OPT: Open Pre-trained Transformer Language Models

#68

Post by dukeofdoom »

ineedasername wrote: post #53
dukeofdoom wrote: post #33

Earlier quoted context omitted.

I haven't used GPT-3, but I did try out a site that was based on GPT2. I believe it was called "talk to transformer". But I never tried quarrying anything controversial. However, I bet this a concern and certain queries will be filtered or "corrected" to be more politically correct. To give you an example, a few days ago I made a comment one Alex Jones, and wanted to google him. The second link returned on him was fr…

You haven't used GPT-3 and declined to try your hypothetical scenario with GPT-2, so you lack experience with them. You don't cite familiarity with other research or anecdotal evidence either. So what exactly is your justification here? Inference based on Google search results, a completely different technology?

Its kind of silly that you even go here. Even though I never used Dall-E, I can still have an opinion about it. Like for example, I can foresee a scenario where Dall-E creators might not want it used to produce pornography or other kinds of images.

Re: OPT: Open Pre-trained Transformer Language Models

#69

Post by VectorLock »

ad_hominem wrote: post #56
thorum wrote: post #31

A quick summary of the Limitations section: - "OPT-175B does not work well with declarative instructions or point-blank interrogatives." - "OPT-175B also tends to be repetitive and can easily get stuck in a loop. While sampling can reduce the incidence rate of repetitive behavior (Holtzman et al., 2020), we anecdotally found it did not eliminate it entirely when only one generation is sampled." - "We also find OPT-17…

> Pushshift.io Reddit corpus Pushshift is a single person with some very strong political opinions who has specifically used his datasets to attack political opponents. Frankly I wouldn't trust his data to be untainted. These models really need to be trained on more official data sources, or at least something with some type of multi-party oversight rather than data that effectively fell off the back of a truck. edit…

Thats interesting, any good sources for this accusation?

Re: OPT: Open Pre-trained Transformer Language Models

#70

Post by freeone3000 »

PeterisP wrote: post #39
faebi wrote: post #26

Earlier quoted context omitted.

Does the model need to be in memory in order to run it with current tooling?

To run it at a reasonable speed, yes. Computing a single word requires all of the parameters; if you don't have them in memory you'd have to re-transfer all those gigabytes to the GPU for each full pass to get some output, which is a severe performance hit as you can't fully use your compute power because the bandwidth is likely to be the bottleneck - running inference for just a single example will take many seconds…

My following post is entirely speculation.

NVLink also gives you memory pooling; 8*32GB just baaarely fits the model. NVBus is the public version of an InfiniBand interconnect allowing for V-RDMA (which people have been doing for years), which would then allow for distributed execution using pydist or Megatron (or DeepSpeed). So it's probably a similar infrastructure to Nvidia's supercomputers, since that's what everyone built before Nvidia started selling them.

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