12 ms·

> warning of a powerful artificial intelligence discovery that they said could threaten humanity, > Given vast computing resources, the new model was able to s

by V__ 3y ago

> warning of a powerful artificial intelligence discovery that they said could threaten humanity, > Given vast computing resources, the new model was able to solve certain mathematical problems, [..] Though only performing math on the level of grade-school students, acing such tests made researchers very optimistic about Q*’s future success, the source said. I somehow expected a bit more.

NewJazz 3y ago
Yeah I was chuckling
- imdsm 3y ago
  It was an "Oh" moment, but on the other hand, it'd be like laughing at text-davinci-003 when you realised how basic it really was, and yet, it has led us to GPT-4 and, much as Windows 3.1 was amazing back in the day, now it's a distant memory as we carry around little glass tablets that can play 3D games and do UHD global video calls. If Q* is the basic algorithm that is as transformative as, well, transformers/attention, then developing it to be more than just children's math could be the key to thinking - and not just latent space token generation.
- mikrl 3y ago
  They called it Q* It’s not the first weird conspiracy I’ve heard of involving a cryptically named figure called Q…
  - LudwigNagasena 3y ago
    AGI doomers can now be referred to as Q*Anon.
hn_throwaway_99 3y ago
I think there is enough there to make me believe it's real. After all, a big objection to LLMs is they are just "fancy autocomplete" or "stochastic parrots". I don't agree, but LLMs famously are "bad at math", that is you can't really train them to do math unless they've seen previous calculations with similar numbers in their training sets. Training an AI system to truly do arbitrary mathematical logic (think "complex word problems with big numbers", not just normal programming statements) would be a huge breakthrough.
- xanderlewis 3y ago
  Mathematical logic doesn’t refer to ‘complex word problems with big numbers’. The reason that LLMs struggle (well, are hopeless currently) with mathematics problems is not because it involves arithmetic (although this is already enough of a struggle) but because it requires a conceptual understanding of the ideas rather than just being about manipulating language — a lot of the goings-on are hidden in the semantic world and aren’t necessarily encoded in the syntax, unlike when writing code.
  - tyingq 3y ago
    I've seen it gets tripped up if you mix something common with something uncommon. ChatGPT chokes on, for example: "Two trains on separate tracks, 30 miles from each other are approaching each other, each at a speed of 10 mph. How long before they crash into each other?"
    - xanderlewis 3y ago
      Yes. Though I’d say that example is a bit mean (it’s a trick question) since the answer has expected type <time> but whose actual answer is something like “don’t be stupid; they’re not even on the same track”. It’s like asking “if I add three apples to two apples, how many pears do I get?” and being surprised when the LLM says “5 pears”. It’s the sort of thing that’s avoidable if the question is written properly.
      - Sohcahtoa82 3y ago
        I think it's completely reasonable to ask an AI that people want to consider intelligent trick questions. If it's really that smart and capable of reasoning, it should identify the trick. Some humans will be fooled by the question, sure. But an AI should be smarter than humans, or at least, as smart as an above-average human.
        xanderlewis 3y ago
        I agree. But you could ask which is more intelligent: recognising a trick question and balking, or recognising that the question as posed doesn’t quite make sense and offering a reformulation together with its answer. It’s not always clear whether something’s a trick, a mistake or a strangely worded (but nonetheless intentionally weird) question. So I think it would be very hard to get it to never fall for any tricks.
dougmwne 3y ago
This makes some sense to me. My experience with GPT is that it is capable of straightforward logical inference, but not more inspired thinking. It lacks the ability for a “eureka moment”. All complex inference it appears to have is a result of its training set. It is incapable of solving certain kinds of logic problems that a child would be able to solve. As an example, take the wolf, goat, and cabbage problem, but change the names of the animals and the situation so that the axioms are intact, but the situation no longer seems similar to the classic problem, and therefore has no representation in the training data. GPT-4 can no longer solve this problem consistently. If Q* can now solve problems like this reliably, it could represent a breakthrough in LLM’s ability to model the world and extrapolate beyond the training data.
- imdsm 3y ago
  And thus the divide between decels and accels came to the fore. The decels wanted to destroy OpenAI (the mission) to stop progress on Q*. This is like a jigsaw but bit by bit, the picture is coming together.
- tsunamifury 3y ago
  GPT4 solves this problem In any combination easily. What do so many posters seem to claim to have stumped it?
  - dougmwne 3y ago
    Because I did stump it, not claim to. Used GPT-4 near when it was released, fed it variations on the problem for about an hour. I found it to be a fascinating exercise because it was teaching be the difference between knowledge (training set) and intelligence (generalization).
    - tsunamifury 3y ago
      http://allofus.ai/journeys/735TTpCUncpAQC9yUqM3ve http://allofus.ai/journeys/735TTpCUncpAQC9yUqM3ve Easily solved it everytime On allofus.ai using basic reflection and mixture of experts.
fullshark 3y ago
My guess is if you see AGI as inevitable, then every step towards it is seen as groundbreaking with unprecedented future impact on humanity.
- falcor84 3y ago
  I'm not following - if you think AGI is uncertain, shouldn't you actually be more surprised? I mean, looking at it from a bayesian lens, the lower your prior, the more (in absolute percentage points) you would need to adjust it based on new supporting evidence, no?
  - fullshark 3y ago
    If you think AGI is uncertain, then maybe it's just an improvement on a paradigm that is near its end state: amazing autocomplete.
    - falcor84 3y ago
      Enlighten me then - what is this limit point of "amazing autocomplete"?
      - falcor84 3y ago
        To clarify, I'm really curious about this question. Is there some limit to autocomplete that falls short of continuing a prompt such as: "The following is a literate programming compendium on how to stimulate the human brain in software ..."
        fullshark 3y ago
        I don't have a good answer for your question, I was just making the point that if you think this is a step toward a dead end and not AGI your attitude regarding the step changes.
        3y ago
LewisVerstappen 3y ago
The entire criticism of LLMs is that they're amazing at recall and bad at reasoning. The discovery OpenAI could've made might've been on techniques to teach LLMs reasoning. That would be very big.
duringmath 3y ago
I can't decide if these people are so conceited they actually believe the AGI nonsense or it's just a cynical marketing ploy to string these reporters along.
- xanderlewis 3y ago
  Why is it nonsense?
qgin 3y ago
Being legitimately good at reasoning when it comes to numbers is a new emergent behavior. Reasoning about numbers isn't something that exists in "idea space" where all the vectorized tokens exist.
- PaulDavisThe1st 3y ago
  > Reasoning about numbers isn't something that exists in "idea space" where all the vectorized tokens exist. To be fair, we don't know for certain that this is the case.
Geee 3y ago
It would be a major breakthrough if the model wasn't trained on math at all. Which I suspect is the case here, because otherwise it doesn't make sense.
XorNot 3y ago
It's the exponent problem though: if it can actually engage in abstract mathematical reasoning, then it's a machine which doesn't need to sleep able to come up with new independent ideas potentially. It's the rudiments of being able to develop and reason about computation, which means it's the rudiments of self-modification and improvement. Which is basically the holy grail of AI: a program which can iteratively improve itself to create better AIs and suddenly we're off to the races. This is before getting into other interesting parameters, like how the scale and components of computer technology have a physical reality, and we've had experiences in the lab of genetic algorithms developing novel "cheat" strategies which exploit the physical characteristics of their hardware.
vjerancrnjak 3y ago
OpenAI already benchmarks their GPTs on leetcode problems and even includes a Codeforces rating. It is not impressive at all and there's almost no progress from GPT 2 to 4. I agree, why does this grade school math problem matter if the model can't solve problems that are very precisely stated and have a very narrow solution space (at least more narrow than some vague natural language instruction)?
- bcherry 3y ago
  I suspect the truth (if this claim is true) is a lot more nuanced than "it did grade-school math", and there's more context surrounding this claim which insiders have and makes it much more interesting.
  - bhy 3y ago
    Like most research they likely started with a smaller model like GPT 2 or 3 and shown that they can significantly boost the performance to the extent of solving grade school math.
- maxdoop 3y ago
  Are you saying GPT-4 is not much different than GPT2?
  - vjerancrnjak 3y ago
    Leetcode and codeforces. AlphaCode is much better.
- sweezyjeezy 3y ago
  Here is something that I think would be a big breakthrough: I explain to GPT in text, a mathematical concept it has never seen in its training data and give a few examples (not inferred from fill the blank on millions of examples). It actually learns this to update its weights - not just uses it as part of a prompt. Extrapolating this optimistically - this is a huge step towards AGI in my opinion. You can (in theory) teach it to automate many tasks, correct it's mistakes without needing costly extra training data, and move towards the few-shot (and persistent) learning that separates humans from AI right now.
Merrill 3y ago
Interesting, because based on my wife's stroke, it appears that the human brain implements language and math somewhat separately. She was a physics major and had completed 4 years of relevant college math courses. After her stroke she lost the ability to do even simple arithmetic and also had great trouble with time and calendars. However, her language skills were essentially intact until a third stroke in a different part of the other hemisphere left her with severe expressive aphasia. (Her first stroke left her with homonymous hemianopsia, but that's a loss of visual preprocessing in the occipital lobe.) So I would not expect LLMs to have math skills unless specifically trained and specialized for math.
- chewxy 3y ago
  That's quite correct. The IP1 and IP2 areas are usually associated with mathematics and production of motor cues around mathematics and stories. The Broca's and Vernicke's Areas are associated with language understanding and production. You can roughly estimate that the human brain's language model roughly fits into a floppy disk (1.44 MB for you young'uns)
zugi 3y ago
None of us have seen the letter so this may be off base, but I would expect people working at the world's most prominent AI research organization to have more skepticism about the ramifications of any one "breakthrough." Perhaps most did, but a couple didn't and wrote the letter? More than 3 decades ago when AI started beating humans at chess, some people feared AGI was right around the corner. They were wrong. Last year a Google researcher thought his chat bot was sentient ( https://www.scientificamerican.com/article/google-engineer-claims-ai-chatbot-is-sentient-why-that-matters/ https://www.scientificamerican.com/article/google-engineer-c... .) He was wrong. Some day AGI will be achieved and Q* sounds like a great breakthrough solving an interesting piece of the puzzle. But "performing math on the level of grade-school students" is a long ways from AGI. This seems like a strange thing to have triggered the chaos at OpenAI.
- famouswaffles 3y ago
  >Last year a Google researcher thought his chat bot was sentient ( https://www.scientificamerican.com/article/google-engineer-c https://www.scientificamerican.com/article/google-engineer-c... .) He was wrong. You've figured out how to test for sentience ?
  - deleted 3y ago
    [deleted]
  - sweezyjeezy 3y ago
    I think the word "sentience" is a red herring. The more important point is that the researcher at Google thought that the AI had wants and needs 'like a human', e.g. that if it asked the AI if it wanted legal representation to protect its own rights, this was the same as asking a human the same question. This needs much stronger evidence than the researcher presented, when slight variations or framing of the same questions could lead to very different outcomes from the LLM.
    - KaoruAoiShiho 3y ago
      > that if it asked the AI if it wanted legal representation to protect its own rights, this was the same as asking a human the same question. You seem to be assigning a level of stupidity to a google AI researcher that doesn't seem wise. That guy is not a crazy who grabbed his 15 minutes and disappeared, he's active on twitter and elsewhere and has extensively defended his views in very cogent ways.
reasonableklout 3y ago
I don't really understand this. Aren't LLMs already performing at near-expert level on "certain mathematical problem" benchmarks? For example, over a year ago MINERVA from Google [1] got >50% on the MATH dataset, a set of competition math problems. These are not easy problems. From the MATH dataset paper: > We also evaluated humans on MATH, and found that a computer science PhD student who does not especially like mathematics attained approximately 40% on MATH, while a three-time IMO gold medalist attained 90%, indicating that MATH can be challenging for humans as well. [1] https://blog.research.google/2022/06/minerva-solving-quantitative-reasoning.html https://blog.research.google/2022/06/minerva-solving-quantit...
- lupire 3y ago
  Every example on that web page is a trivial textbook problem. That shows memorization of training set and textual pattern matching.
  - reasonableklout 3y ago
    No, that shows only that the dataset is comprised of common problem patterns. The paper explicitly investigates whether memorization has overly impacted performance. From page 10 [1]: > A central question in interpreting Minerva’s solutions is whether performance reflects genuine analytic capability or instead rote memorization. This is especially relevant as there has been much prior work indicating that language models often memorize some fraction of their training data ... In order to evaluate the degree to which our models solve problems by recalling information memorized from training data, we conduct three analyses on the MATH dataset ... Overall, we find little evidence that the model’s performance can be attributed to memorization. [1] https://arxiv.org/pdf/2206.14858.pdf https://arxiv.org/pdf/2206.14858.pdf
    - riku_iki 3y ago
      in Appendix j.2. they say that accuracy degraded after modification, figure 11 shows that accuracy degraded in 15 out of 20 examples after large modification.
deleted 3y ago
[deleted]

by V__ 3y ago

NewJazz 3y ago

Yeah I was chuckling

imdsm 3y ago
It was an "Oh" moment, but on the other hand, it'd be like laughing at text-davinci-003 when you realised how basic it really was, and yet, it has led us to GPT-4 and, much as Windows 3.1 was amazing back in the day, now it's a distant memory as we carry around little glass tablets that can play 3D games and do UHD global video calls. If Q* is the basic algorithm that is as transformative as, well, transformers/attention, then developing it to be more than just children's math could be the key to thinking - and not just latent space token generation.
mikrl 3y ago
They called it Q* It’s not the first weird conspiracy I’ve heard of involving a cryptically named figure called Q…
- LudwigNagasena 3y ago
  AGI doomers can now be referred to as Q*Anon.

hn_throwaway_99 3y ago

I think there is enough there to make me believe it's real. After all, a big objection to LLMs is they are just "fancy autocomplete" or "stochastic parrots". I don't agree, but LLMs famously are "bad at math", that is you can't really train them to do math unless they've seen previous calculations with similar numbers in their training sets. Training an AI system to truly do arbitrary mathematical logic (think "complex word problems with big numbers", not just normal programming statements) would be a huge breakthrough.

xanderlewis 3y ago
Mathematical logic doesn’t refer to ‘complex word problems with big numbers’. The reason that LLMs struggle (well, are hopeless currently) with mathematics problems is not because it involves arithmetic (although this is already enough of a struggle) but because it requires a conceptual understanding of the ideas rather than just being about manipulating language — a lot of the goings-on are hidden in the semantic world and aren’t necessarily encoded in the syntax, unlike when writing code.
- tyingq 3y ago
  I've seen it gets tripped up if you mix something common with something uncommon. ChatGPT chokes on, for example: "Two trains on separate tracks, 30 miles from each other are approaching each other, each at a speed of 10 mph. How long before they crash into each other?"
  - xanderlewis 3y ago
    Yes. Though I’d say that example is a bit mean (it’s a trick question) since the answer has expected type <time> but whose actual answer is something like “don’t be stupid; they’re not even on the same track”. It’s like asking “if I add three apples to two apples, how many pears do I get?” and being surprised when the LLM says “5 pears”. It’s the sort of thing that’s avoidable if the question is written properly.
    - Sohcahtoa82 3y ago
      I think it's completely reasonable to ask an AI that people want to consider intelligent trick questions. If it's really that smart and capable of reasoning, it should identify the trick. Some humans will be fooled by the question, sure. But an AI should be smarter than humans, or at least, as smart as an above-average human.
      - xanderlewis 3y ago
        I agree. But you could ask which is more intelligent: recognising a trick question and balking, or recognising that the question as posed doesn’t quite make sense and offering a reformulation together with its answer. It’s not always clear whether something’s a trick, a mistake or a strangely worded (but nonetheless intentionally weird) question. So I think it would be very hard to get it to never fall for any tricks.

dougmwne 3y ago

This makes some sense to me. My experience with GPT is that it is capable of straightforward logical inference, but not more inspired thinking. It lacks the ability for a “eureka moment”. All complex inference it appears to have is a result of its training set. It is incapable of solving certain kinds of logic problems that a child would be able to solve. As an example, take the wolf, goat, and cabbage problem, but change the names of the animals and the situation so that the axioms are intact, but the situation no longer seems similar to the classic problem, and therefore has no representation in the training data. GPT-4 can no longer solve this problem consistently. If Q* can now solve problems like this reliably, it could represent a breakthrough in LLM’s ability to model the world and extrapolate beyond the training data.

imdsm 3y ago
And thus the divide between decels and accels came to the fore. The decels wanted to destroy OpenAI (the mission) to stop progress on Q*. This is like a jigsaw but bit by bit, the picture is coming together.
tsunamifury 3y ago
GPT4 solves this problem In any combination easily. What do so many posters seem to claim to have stumped it?
- dougmwne 3y ago
  Because I did stump it, not claim to. Used GPT-4 near when it was released, fed it variations on the problem for about an hour. I found it to be a fascinating exercise because it was teaching be the difference between knowledge (training set) and intelligence (generalization).
  - tsunamifury 3y ago
    http://allofus.ai/journeys/735TTpCUncpAQC9yUqM3ve http://allofus.ai/journeys/735TTpCUncpAQC9yUqM3ve Easily solved it everytime On allofus.ai using basic reflection and mixture of experts.

fullshark 3y ago

My guess is if you see AGI as inevitable, then every step towards it is seen as groundbreaking with unprecedented future impact on humanity.

falcor84 3y ago
I'm not following - if you think AGI is uncertain, shouldn't you actually be more surprised? I mean, looking at it from a bayesian lens, the lower your prior, the more (in absolute percentage points) you would need to adjust it based on new supporting evidence, no?
- fullshark 3y ago
  If you think AGI is uncertain, then maybe it's just an improvement on a paradigm that is near its end state: amazing autocomplete.
  - falcor84 3y ago
    Enlighten me then - what is this limit point of "amazing autocomplete"?
    - falcor84 3y ago
      To clarify, I'm really curious about this question. Is there some limit to autocomplete that falls short of continuing a prompt such as: "The following is a literate programming compendium on how to stimulate the human brain in software ..."
      - fullshark 3y ago
        I don't have a good answer for your question, I was just making the point that if you think this is a step toward a dead end and not AGI your attitude regarding the step changes.
        3y ago

LewisVerstappen 3y ago

The entire criticism of LLMs is that they're amazing at recall and bad at reasoning. The discovery OpenAI could've made might've been on techniques to teach LLMs reasoning. That would be very big.

duringmath 3y ago

I can't decide if these people are so conceited they actually believe the AGI nonsense or it's just a cynical marketing ploy to string these reporters along.

xanderlewis 3y ago
Why is it nonsense?

qgin 3y ago

Being legitimately good at reasoning when it comes to numbers is a new emergent behavior. Reasoning about numbers isn't something that exists in "idea space" where all the vectorized tokens exist.

PaulDavisThe1st 3y ago
> Reasoning about numbers isn't something that exists in "idea space" where all the vectorized tokens exist. To be fair, we don't know for certain that this is the case.

Geee 3y ago

It would be a major breakthrough if the model wasn't trained on math at all. Which I suspect is the case here, because otherwise it doesn't make sense.

XorNot 3y ago

It's the exponent problem though: if it can actually engage in abstract mathematical reasoning, then it's a machine which doesn't need to sleep able to come up with new independent ideas potentially. It's the rudiments of being able to develop and reason about computation, which means it's the rudiments of self-modification and improvement. Which is basically the holy grail of AI: a program which can iteratively improve itself to create better AIs and suddenly we're off to the races. This is before getting into other interesting parameters, like how the scale and components of computer technology have a physical reality, and we've had experiences in the lab of genetic algorithms developing novel "cheat" strategies which exploit the physical characteristics of their hardware.

vjerancrnjak 3y ago

OpenAI already benchmarks their GPTs on leetcode problems and even includes a Codeforces rating. It is not impressive at all and there's almost no progress from GPT 2 to 4. I agree, why does this grade school math problem matter if the model can't solve problems that are very precisely stated and have a very narrow solution space (at least more narrow than some vague natural language instruction)?

bcherry 3y ago
I suspect the truth (if this claim is true) is a lot more nuanced than "it did grade-school math", and there's more context surrounding this claim which insiders have and makes it much more interesting.
- bhy 3y ago
  Like most research they likely started with a smaller model like GPT 2 or 3 and shown that they can significantly boost the performance to the extent of solving grade school math.
maxdoop 3y ago
Are you saying GPT-4 is not much different than GPT2?
- vjerancrnjak 3y ago
  Leetcode and codeforces. AlphaCode is much better.
sweezyjeezy 3y ago
Here is something that I think would be a big breakthrough: I explain to GPT in text, a mathematical concept it has never seen in its training data and give a few examples (not inferred from fill the blank on millions of examples). It actually learns this to update its weights - not just uses it as part of a prompt. Extrapolating this optimistically - this is a huge step towards AGI in my opinion. You can (in theory) teach it to automate many tasks, correct it's mistakes without needing costly extra training data, and move towards the few-shot (and persistent) learning that separates humans from AI right now.

Merrill 3y ago

Interesting, because based on my wife's stroke, it appears that the human brain implements language and math somewhat separately. She was a physics major and had completed 4 years of relevant college math courses. After her stroke she lost the ability to do even simple arithmetic and also had great trouble with time and calendars. However, her language skills were essentially intact until a third stroke in a different part of the other hemisphere left her with severe expressive aphasia. (Her first stroke left her with homonymous hemianopsia, but that's a loss of visual preprocessing in the occipital lobe.) So I would not expect LLMs to have math skills unless specifically trained and specialized for math.

chewxy 3y ago
That's quite correct. The IP1 and IP2 areas are usually associated with mathematics and production of motor cues around mathematics and stories. The Broca's and Vernicke's Areas are associated with language understanding and production. You can roughly estimate that the human brain's language model roughly fits into a floppy disk (1.44 MB for you young'uns)

zugi 3y ago

None of us have seen the letter so this may be off base, but I would expect people working at the world's most prominent AI research organization to have more skepticism about the ramifications of any one "breakthrough." Perhaps most did, but a couple didn't and wrote the letter? More than 3 decades ago when AI started beating humans at chess, some people feared AGI was right around the corner. They were wrong. Last year a Google researcher thought his chat bot was sentient ( https://www.scientificamerican.com/article/google-engineer-claims-ai-chatbot-is-sentient-why-that-matters/ https://www.scientificamerican.com/article/google-engineer-c... .) He was wrong. Some day AGI will be achieved and Q* sounds like a great breakthrough solving an interesting piece of the puzzle. But "performing math on the level of grade-school students" is a long ways from AGI. This seems like a strange thing to have triggered the chaos at OpenAI.

famouswaffles 3y ago
>Last year a Google researcher thought his chat bot was sentient ( https://www.scientificamerican.com/article/google-engineer-c https://www.scientificamerican.com/article/google-engineer-c... .) He was wrong. You've figured out how to test for sentience ?
- deleted 3y ago
  [deleted]
- sweezyjeezy 3y ago
  I think the word "sentience" is a red herring. The more important point is that the researcher at Google thought that the AI had wants and needs 'like a human', e.g. that if it asked the AI if it wanted legal representation to protect its own rights, this was the same as asking a human the same question. This needs much stronger evidence than the researcher presented, when slight variations or framing of the same questions could lead to very different outcomes from the LLM.
  - KaoruAoiShiho 3y ago
    > that if it asked the AI if it wanted legal representation to protect its own rights, this was the same as asking a human the same question. You seem to be assigning a level of stupidity to a google AI researcher that doesn't seem wise. That guy is not a crazy who grabbed his 15 minutes and disappeared, he's active on twitter and elsewhere and has extensively defended his views in very cogent ways.

reasonableklout 3y ago

I don't really understand this. Aren't LLMs already performing at near-expert level on "certain mathematical problem" benchmarks? For example, over a year ago MINERVA from Google [1] got >50% on the MATH dataset, a set of competition math problems. These are not easy problems. From the MATH dataset paper: > We also evaluated humans on MATH, and found that a computer science PhD student who does not especially like mathematics attained approximately 40% on MATH, while a three-time IMO gold medalist attained 90%, indicating that MATH can be challenging for humans as well. [1] https://blog.research.google/2022/06/minerva-solving-quantitative-reasoning.html https://blog.research.google/2022/06/minerva-solving-quantit...

lupire 3y ago
Every example on that web page is a trivial textbook problem. That shows memorization of training set and textual pattern matching.
- reasonableklout 3y ago
  No, that shows only that the dataset is comprised of common problem patterns. The paper explicitly investigates whether memorization has overly impacted performance. From page 10 [1]: > A central question in interpreting Minerva’s solutions is whether performance reflects genuine analytic capability or instead rote memorization. This is especially relevant as there has been much prior work indicating that language models often memorize some fraction of their training data ... In order to evaluate the degree to which our models solve problems by recalling information memorized from training data, we conduct three analyses on the MATH dataset ... Overall, we find little evidence that the model’s performance can be attributed to memorization. [1] https://arxiv.org/pdf/2206.14858.pdf https://arxiv.org/pdf/2206.14858.pdf
  - riku_iki 3y ago
    in Appendix j.2. they say that accuracy degraded after modification, figure 11 shows that accuracy degraded in 15 out of 20 examples after large modification.

deleted 3y ago

[deleted]