This is the paper surrounding Timnit's "departure" from Google.
If you're on Timnit's side, "departure" means "firing", and the paper is the reason she was fired.
If you're on Google's side, "departure" means "mutually-agreeable resignation", with Timnit's melodramatic and unprofessional response to normal feedback.
Personally, I don't see anything in this paper that implicates Google or would be reasonable for Google to try to suppress, so I'm falling into the camp of trusting Google's side of the story. But who knows?
> Personally, I don't see anything in this paper that implicates Google or would be reasonable for Google to try to suppress
Can you explain why this leads you to support Google? Google still claims that the paper doesn't meet their publication standards, despite, as you say, it containing nothing that it would be reasonable for Google to suppress.
> Timnit's melodramatic and unprofessional response to normal feedback.
Keep in mind the feedback was that you cannot publish this paper, and that isn't disputed by Google.
As a general principle, when there is a "they-said she-said" situation, and the certain facts about one side of the story don't add up, from my perspective that increases the odds that the other side is telling the truth.
I have no idea what Google's standards are for letting a paper be published. My guess is that they don't make standards up on the fly, and no unique standards are applied to Timnit and not to literally everyone else.
> Keep in mind the feedback was that you cannot publish this paper, and that isn't disputed by Google.
Yes, and that in itself makes me more likely to think there is some benign, standard reason for not allowing publication rather than some nefarious motive about suppressing Timnit or hiding their own guilt, or whatever reason Timnit has provided as to why she thinks Google doesn't want the paper published.
If the argument is "Google won't let me publish this paper because it's too dangerous for them", and then I look and see that there isn't really anything dangerous for Google in the paper, then I would say that perhaps the argument is incorrect as to the reasons Google wouldn't let them publish
What? If someone says a paper is too dangerous for Google, but then I read the paper and there is nothing dangerous at all for Google - that is evidence that there is in fact something too dangerous for Google in the paper?
This paper was originally going to be coauthored by Timnit Gebru, Margaret Mitchell, Emily Bender, and a few other collaborators from Google and UW.
The paper, at a high level, offers criticisms of large language models (including BERT, a Google model). In ~October/November, the paper went through the normal Google paper review process and was approved to be published externally (i.e. submitted to a conference). Later, Gebru was informed that the paper was not fit for publication due to some additional review, and needed to be unsubmitted from the conference, or the Google coauthors needed to removed their names. Initially, this was provided with no context or reason.
Upon pushing back, Gebru was given some information on why the paper was unfit for publication. Publicly, what we know is that Google's reasoning here was that the paper did not cite relevant work and was not up to Google's publication standards (of note here, the paper cites nearly 200 other works, which is huge for a CS paper, and it later passed peer review at the conference, so this claim seems dubious).
Gebru complained that this feedback was essentially trying to bury the paper, especially given that she was not given the opportunity to address or incorporate the feedback, only drop the paper. She sent two emails, one to her management stating that this kind of process was not conducive to research and stating that she would consider resigning if things didn't change. She also sent an email complaining about the process to a mailing list about diversity and inclusion work, noting that DE&I work was going to continue to be a waste of time without executive buy-in.
Google "accepted Gebru's resignation", noting that the second email she sent was unprofessional. Under the relevant law, Gebru didn't resign and was fired by Google. Google has since partially walked back their statements, and refers to the situation as her "departure", leaving it amusingly vague.
The paper is published in January, after passing peer review, coauthroed by "Schmargaret Schmitchell", among others. It's since come to light that some other papers have also gone through this additional review since then, this additional review process was formalized, it seems, only after Gebru's paper was submitted. The sensitive review process involves legal approval and requires authors to remove statements like "having concerns". [0]
Margaret Mitchell, Gebru's co-lead is also fired, for sharing confidential information. The investigation into her misconduct took over a month, and she was fired the same day as a reorganization of the AI ethics team was announced.
Gebru also publicly accused Jeff Dean of being complicit in "silenc[ing]" female researchers over a year before she resigned/was fired [1], and the "email complaining about the process to a mailing list about diversity and inclusion work" also included a brief reference to past legal tussles between herself and Google:
> I’m always amazed at how people can continue to do thing after thing like this and then turn around and ask me for some sort of extra DEI work or input. This happened to me last year. I was in the middle of a potential lawsuit for which Kat Herller and I hired feminist lawyers who threatened to sue Google (which is when they backed off--before that Google lawyers were prepared to throw us under the bus and our leaders were following as instructed) and the next day I get some random “impact award.” Pure gaslighting.
From the outside, it looks like whatever relationship Gebru and Google leadership had was extremely strained well before this paper. It looks like Google leadership had gotten tired of Gebru (for good or bad reasons) and took this as a good time to cut ties.
Google didn't fire Dr Gebru for this paper, even though that is the popular narrative. Google fired her for sending an email to a large list that said Google's DEI initiatives were a failure and that employees shouldn't waste their time contributing to them.
If Google execs believe that AIs trained on the public Web are the future of Google, this paper basically argues that those AIs, and by extension Google's future, are unethical and probably can't be fixed at any reasonable cost.
The paper mentions "... similar to the ones used in GPT-2’s training data, i.e. documents linked to from Reddit [25], plus Wikipedia and a collection of books". Does anyone know what collection of books they are talking about?
I tried following the chain of references but ended up at a pay-walled source. Is it based on project gutenberg? Also, does Google train their models on the contents of all the books they scanned for Google Books or are they not allowed to because of copyright right issues?
The "bookcorpus" is from this paper by Zhu, Kiro, et. al. [0]. You can see the project web page here [1] which indicates you can crawl books from this site [2] to create your own. This repo seeks to replicate the original dataset [3]
To speculate a bit:
The GPT-3 paper (section 2.2) mentions using two datasets referred to as "books1" and "books2", which are 12B and 55B byte pair encoded tokens each.
Project Gutenberg has 3B word tokens I believe, so it seems like it could be one of them, assuming the ratio of word tokens to byte-pair tokens is something like 3:12 to 3:55.
Another likely candidate alongside Gutenberg is libgen, apparently, and looks like there have been successful efforts to create a similar dataset called bookcorpus: https://github.com/soskek/bookcorpus/issues/27). The discussion on that github issue suggests bookcorpus is very similar to "books2", which would make gutenberg "books1"?
This might be why the paper is intentionally vague about the books used?
It's still a mystery, yes. It's annoying, but not particularly important, as EleutherAI has created a more open substitute with the necessary size to train a GPT-3-like model, as The Pile: https://pile.eleuther.ai/ (In some ways it is probably better than OA's mystery-meat corpus.)
"The Slodderwetenschap (Sloppy Science) of Stochastic Parrots – A Plea for Science to NOT take the Route Advocated by Gebru and Bender" by Michael Lissack.
reading it I thought - if language models can be too big that could be a problem for Google given that at least one of their major competitive advantages is being able to have the biggest language models there are.
Although I don't really know if that's so (about the competitive advantage), it certainly seems like it is something Google might think from what I remember about earlier Google arguments about automated translation.
You don't need math to explain why, for example, a statistical model trained to find the plausible combinations of words or phrases in its corpus that matches a prompt is not a promising approach to NLP and general understanding, and using it to win at tasks designed to assess NLP success essentialy amounts to cheating. You also don't need math to explain that, in very real terms, the meaning of any phrase produced by GPT-3 lies in the mind of the reader and not in GPT-3's output, which doesn't see any meaning in the phrases it produces (unlike, say, the output of a rule-based system, which, while much more rudimentary, generally has reasoning about the topic at hand behind it, and not about word probabilities).
> to explain why, for example, [...] is not a promising approach to NLP and general understanding
I am not aware of any definitive proof or at least convincing argument of why this is not possible, though. Timnit's paper takes that as a given fact and illustrates consequent dangers and high-level workarounds.
I didn't say that it's not possible, just that it's not likely. It's clearly not how the human mind works, and since that is the only computer we know for sure can do NLP, any approach that is so obviously alien to it is unlikely to be a good approach.
In case this is not clear, the human mind obviously doesn't do stochastic prediction on phrases, it has a model of the world ( based on agents interacting with objects through mechanical laws) and it produces or interprets speech by assessing its interaction with this model of the world. When assessing whether to produce phrase A or phrase B, it doesn't assess the likelihood of this phrase in the corpus of phrases it has seen/heard before, but on its appropriateness to the situation. Most prompts for language use are not language at all, but come from the world itself [0], something which pure LMs can't even in principle do (they they could potentially be combined with other kinds of models to achieve this).
[0] for example, 'seeing a fire in a crowded theater' is the prompt for yelling 'Fire!'; the correct response/next step from hearing a shout of 'Fire!' in a crowded theater is not a phrase, it is a desperate attempt to run away
> In case this is not clear, the human mind obviously doesn't do stochastic prediction on phrases.
Are you sure? Because this is exactly what you and I are doing right now. A language model is a very appropriate description for how we see each other: you produce some text, I produce some more, and you respond based on that. And I inject some stochasticity by re-typing this paragraph five times trying to make it sound like natural English and a cohesive text, a bit like beam search if you are familiar with that.
What I am trying to say is that language models are a good interface (in the programming sense) to describe human interactions on the internet: text in, text out. So while I agree that GPT-3 is not a realistic "human-like" implementation of this interface, I don't see why a priori a neural network cannot eventually incorporate world models with agents and so on and reach actual understanding (whatever that means) of text.
We may be agreed in fact, to some extent at least. My claim, and I believe the article's as well, is that this model of the world and agents and everything will not probably be learned just by consuming ever larger quantities of text and adding ever more parameters to the model.
But I would also claim that our conversation now is unlikely to be predictable without some model of the world. It's not 'you produce some text, I produce some text', it's 'you produce some text, I run it through my model of the world, my inference engine produces some other model of the world, and then that gets translated to some text in reply'.
any approach that is so obviously alien to it is unlikely to be a good approach
Computers very frequently do not solve problems the same way humans do, so I'm not sure that's a significant point against any particular modeling technique.
Otherwise, if you're waiting for language models to understand language he same way that human minds do, you're probably waiting for AGI, not any particular breakthrough in NLP alone.
Sure, but without something more concrete to support criticisms, it's only a theory, however compelling the reasoning, that this might be a bad approach. Right now it reads like a detailed literature review. Such things are usually the starting point for research, not and end in themselves. I think the authors make a promising start here, but I look forward to further work from them so that if their criticisms are correct the field can turn to more fruitful approaches.
Apart from the external dangers described (social, environmental), which I'm sure many will disagree with on multiple grounds, the article in general raises some very good points about the internal dangers these models pose to the field of NLP itself:
> The problem is, if one side of the communication does not have meaning, then the comprehension of the
implicit meaning is an illusion arising from our singular human
understanding of language (independent of the model). Contrary to how it may seem when we observe its output, an LM is a system
for haphazardly stitching together sequences of linguistic forms
it has observed in its vast training data, according to probabilistic
information about how they combine, but without any reference to
meaning: a stochastic parrot.
> However, from
the perspective of work on language technology, it is far from clear
that all of the effort being put into using large LMs to ‘beat’ tasks
designed to test natural language understanding, and all of the
effort to create new such tasks, once the existing ones have been
bulldozed by the LMs, brings us any closer to long-term goals of
general language understanding systems. If a large LM, endowed
with hundreds of billions of parameters and trained on a very large
dataset, can manipulate linguistic form well enough to cheat its
way through tests meant to require language understanding, have
we learned anything of value about how to build machine language
understanding or have we been led down the garden path?
Designing benchmarks and metrics to test for deep understanding is really hard! Even our education system is nowhere close to a good solution. It would probably warrant an entire research field, maybe using NLP systems to evaluate how good benchmarks are at evaluating understanding.
In a recent conversation regarding colors, my three year old explained to me that clear:color as silent:sound. I thought this showed a pretty good grasp of meaning between these concepts. I have no idea if he came up with this "meaning" himself or if he learned it from some input corpus(likely the netflix kids show corpus).
Later he related that because noises become quieter when they are far away, colors become more transparent when they are far away. This one I am pretty sure he came up with himself, as it is conflating "small angular size" with "transparent".
My point is it not so easy to tell when humans are behaving as stochastic parrots, or when they produce language based on meaning. Indeed, this may be a distinction without a difference.
I think this is a perfect example of the difference, in fact. The first, corect, example could be either, but the second example shows a kind of mistake that couldn't really be made by a language model. Your son used (faulty) inference to deduce (1) silence:sound as clear:color, (2) distance => silence, therefore (3) distance => transparency. Premises 1 and 2 may be in the language training corpus, but conclusion 3 is definitely not, but still he produced this sentence.
Another example is that human language use is clearly related to non-verbal situations. You can show me two fruit and I can say which is bigger without any language prompting.
It may be true that our language faculty is in fact acting similarly to these LMs, but the overall human mind must somehow include a component that translates from our senses to language and back to motor control. There may or may not be other components as well, but this part is definitely required, and highly non-trivial.
40 comments
[ 3.2 ms ] story [ 96.5 ms ] threadIf you're on Timnit's side, "departure" means "firing", and the paper is the reason she was fired.
If you're on Google's side, "departure" means "mutually-agreeable resignation", with Timnit's melodramatic and unprofessional response to normal feedback.
Personally, I don't see anything in this paper that implicates Google or would be reasonable for Google to try to suppress, so I'm falling into the camp of trusting Google's side of the story. But who knows?
Can you explain why this leads you to support Google? Google still claims that the paper doesn't meet their publication standards, despite, as you say, it containing nothing that it would be reasonable for Google to suppress.
> Timnit's melodramatic and unprofessional response to normal feedback.
Keep in mind the feedback was that you cannot publish this paper, and that isn't disputed by Google.
I have no idea what Google's standards are for letting a paper be published. My guess is that they don't make standards up on the fly, and no unique standards are applied to Timnit and not to literally everyone else.
> Keep in mind the feedback was that you cannot publish this paper, and that isn't disputed by Google.
Yes, and that in itself makes me more likely to think there is some benign, standard reason for not allowing publication rather than some nefarious motive about suppressing Timnit or hiding their own guilt, or whatever reason Timnit has provided as to why she thinks Google doesn't want the paper published.
If the argument is "Google won't let me publish this paper because it's too dangerous for them", and then I look and see that there isn't really anything dangerous for Google in the paper, then I would say that perhaps the argument is incorrect as to the reasons Google wouldn't let them publish
It is not disputed by Google that special standards (an additional, non-standard review process) were applied to this paper. There is some lack of clarity about how often that additional review process is applied (https://artificialintelligence-news.com/2020/12/24/google-te..., https://www.reuters.com/article/us-alphabet-google-research-...). But broadly it seems to not have much to do with the technical merit of the papers, despite what Google originally claimed, and instead be a legal/PR process.
This paper was originally going to be coauthored by Timnit Gebru, Margaret Mitchell, Emily Bender, and a few other collaborators from Google and UW.
The paper, at a high level, offers criticisms of large language models (including BERT, a Google model). In ~October/November, the paper went through the normal Google paper review process and was approved to be published externally (i.e. submitted to a conference). Later, Gebru was informed that the paper was not fit for publication due to some additional review, and needed to be unsubmitted from the conference, or the Google coauthors needed to removed their names. Initially, this was provided with no context or reason.
Upon pushing back, Gebru was given some information on why the paper was unfit for publication. Publicly, what we know is that Google's reasoning here was that the paper did not cite relevant work and was not up to Google's publication standards (of note here, the paper cites nearly 200 other works, which is huge for a CS paper, and it later passed peer review at the conference, so this claim seems dubious).
Gebru complained that this feedback was essentially trying to bury the paper, especially given that she was not given the opportunity to address or incorporate the feedback, only drop the paper. She sent two emails, one to her management stating that this kind of process was not conducive to research and stating that she would consider resigning if things didn't change. She also sent an email complaining about the process to a mailing list about diversity and inclusion work, noting that DE&I work was going to continue to be a waste of time without executive buy-in.
Google "accepted Gebru's resignation", noting that the second email she sent was unprofessional. Under the relevant law, Gebru didn't resign and was fired by Google. Google has since partially walked back their statements, and refers to the situation as her "departure", leaving it amusingly vague.
The paper is published in January, after passing peer review, coauthroed by "Schmargaret Schmitchell", among others. It's since come to light that some other papers have also gone through this additional review since then, this additional review process was formalized, it seems, only after Gebru's paper was submitted. The sensitive review process involves legal approval and requires authors to remove statements like "having concerns". [0]
Margaret Mitchell, Gebru's co-lead is also fired, for sharing confidential information. The investigation into her misconduct took over a month, and she was fired the same day as a reorganization of the AI ethics team was announced.
[0]: https://www.reuters.com/article/us-alphabet-google-research-...
> I’m always amazed at how people can continue to do thing after thing like this and then turn around and ask me for some sort of extra DEI work or input. This happened to me last year. I was in the middle of a potential lawsuit for which Kat Herller and I hired feminist lawyers who threatened to sue Google (which is when they backed off--before that Google lawyers were prepared to throw us under the bus and our leaders were following as instructed) and the next day I get some random “impact award.” Pure gaslighting.
From the outside, it looks like whatever relationship Gebru and Google leadership had was extremely strained well before this paper. It looks like Google leadership had gotten tired of Gebru (for good or bad reasons) and took this as a good time to cut ties.
[1] https://twitter.com/timnitgebru/status/1193238414742548480?l...
[2] https://www.platformer.news/p/the-withering-email-that-got-a...
https://www.theguardian.com/technology/2021/feb/19/google-fi...
I tried following the chain of references but ended up at a pay-walled source. Is it based on project gutenberg? Also, does Google train their models on the contents of all the books they scanned for Google Books or are they not allowed to because of copyright right issues?
[0] https://www.cv-foundation.org/openaccess/content_iccv_2015/p...
[1] https://yknzhu.wixsite.com/mbweb
[2] https://www.smashwords.com/
[3] https://github.com/sgraaf/Replicate-Toronto-BookCorpus
Search results sent me to this Twitter thread which suggests it was still a mystery in Aug 2020: https://twitter.com/vboykis/status/1290030614410702848?lang=...
To speculate a bit: The GPT-3 paper (section 2.2) mentions using two datasets referred to as "books1" and "books2", which are 12B and 55B byte pair encoded tokens each.
Project Gutenberg has 3B word tokens I believe, so it seems like it could be one of them, assuming the ratio of word tokens to byte-pair tokens is something like 3:12 to 3:55.
Another likely candidate alongside Gutenberg is libgen, apparently, and looks like there have been successful efforts to create a similar dataset called bookcorpus: https://github.com/soskek/bookcorpus/issues/27). The discussion on that github issue suggests bookcorpus is very similar to "books2", which would make gutenberg "books1"?
This might be why the paper is intentionally vague about the books used?
"The Slodderwetenschap (Sloppy Science) of Stochastic Parrots – A Plea for Science to NOT take the Route Advocated by Gebru and Bender" by Michael Lissack.
https://arxiv.org/ftp/arxiv/papers/2101/2101.10098.pdf
I found this a reasonable critique of the original, despite apparent TOS violations by Lissack leading to his Twitter account being locked.
Although I don't really know if that's so (about the competitive advantage), it certainly seems like it is something Google might think from what I remember about earlier Google arguments about automated translation.
I am not aware of any definitive proof or at least convincing argument of why this is not possible, though. Timnit's paper takes that as a given fact and illustrates consequent dangers and high-level workarounds.
In case this is not clear, the human mind obviously doesn't do stochastic prediction on phrases, it has a model of the world ( based on agents interacting with objects through mechanical laws) and it produces or interprets speech by assessing its interaction with this model of the world. When assessing whether to produce phrase A or phrase B, it doesn't assess the likelihood of this phrase in the corpus of phrases it has seen/heard before, but on its appropriateness to the situation. Most prompts for language use are not language at all, but come from the world itself [0], something which pure LMs can't even in principle do (they they could potentially be combined with other kinds of models to achieve this).
[0] for example, 'seeing a fire in a crowded theater' is the prompt for yelling 'Fire!'; the correct response/next step from hearing a shout of 'Fire!' in a crowded theater is not a phrase, it is a desperate attempt to run away
Are you sure? Because this is exactly what you and I are doing right now. A language model is a very appropriate description for how we see each other: you produce some text, I produce some more, and you respond based on that. And I inject some stochasticity by re-typing this paragraph five times trying to make it sound like natural English and a cohesive text, a bit like beam search if you are familiar with that.
What I am trying to say is that language models are a good interface (in the programming sense) to describe human interactions on the internet: text in, text out. So while I agree that GPT-3 is not a realistic "human-like" implementation of this interface, I don't see why a priori a neural network cannot eventually incorporate world models with agents and so on and reach actual understanding (whatever that means) of text.
But I would also claim that our conversation now is unlikely to be predictable without some model of the world. It's not 'you produce some text, I produce some text', it's 'you produce some text, I run it through my model of the world, my inference engine produces some other model of the world, and then that gets translated to some text in reply'.
Computers very frequently do not solve problems the same way humans do, so I'm not sure that's a significant point against any particular modeling technique.
Otherwise, if you're waiting for language models to understand language he same way that human minds do, you're probably waiting for AGI, not any particular breakthrough in NLP alone.
> The problem is, if one side of the communication does not have meaning, then the comprehension of the implicit meaning is an illusion arising from our singular human understanding of language (independent of the model). Contrary to how it may seem when we observe its output, an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.
> However, from the perspective of work on language technology, it is far from clear that all of the effort being put into using large LMs to ‘beat’ tasks designed to test natural language understanding, and all of the effort to create new such tasks, once the existing ones have been bulldozed by the LMs, brings us any closer to long-term goals of general language understanding systems. If a large LM, endowed with hundreds of billions of parameters and trained on a very large dataset, can manipulate linguistic form well enough to cheat its way through tests meant to require language understanding, have we learned anything of value about how to build machine language understanding or have we been led down the garden path?
Later he related that because noises become quieter when they are far away, colors become more transparent when they are far away. This one I am pretty sure he came up with himself, as it is conflating "small angular size" with "transparent".
My point is it not so easy to tell when humans are behaving as stochastic parrots, or when they produce language based on meaning. Indeed, this may be a distinction without a difference.
Another example is that human language use is clearly related to non-verbal situations. You can show me two fruit and I can say which is bigger without any language prompting.
It may be true that our language faculty is in fact acting similarly to these LMs, but the overall human mind must somehow include a component that translates from our senses to language and back to motor control. There may or may not be other components as well, but this part is definitely required, and highly non-trivial.