7 ms·

This article is weak and just general speculation. Many people doubt the actual performance of Grok 3 and suspect it has been specifically trained on the bench

by bambax 2y ago

This article is weak and just general speculation. Many people doubt the actual performance of Grok 3 and suspect it has been specifically trained on the benchmarks. And Sabine Hossenfelder says this: > Asked Grok 3 to explain Bell's theorem. It gets it wrong just like all other LLMs I have asked because it just repeats confused stuff that has been written elsewhere rather than looking at the actual theorem. https://x.com/skdh/status/1892432032644354192 https://x.com/skdh/status/1892432032644354192 Which shows that "massive scaling", even enormous, gigantic scaling, doesn't improve intelligence one bit; it improves scope, maybe, or flexibility, or coverage, or something, but not "intelligence".

melodyogonna 2y ago
How can it be specifically trained on benchmarks when it is leading on blind chatbot tests? The post you quoted is not a Grok problem if other LLMs are also failing, it seems, to me, to be a fundamental failure in the current approach to AI model development.
- bearjaws 2y ago
  Any LLM that is uncensored does well on Chatbot tests because a refusal is an automatic loss. And since 30% of people using Chatbots are Gooning it up theres far more refusals...
  - pyinstallwoes 2y ago
    Gooning?
    - bearjaws 2y ago
      https://www.urbandictionary.com/define.php?term=gooning https://www.urbandictionary.com/define.php?term=gooning
- nycdatasci 2y ago
  I think a more plausible path to gaming benchmarks would be to use watermarks in text output to identify your model, then unleash bots to consistently rank your model over opponents.
ttoinou 2y ago
Many people doubt the actual performance of Grok 3 and suspect it has been specifically trained on the benchmarks That's something I always wondered about, Goodhart's law is so obvious to apply to each new AI release. Even the fact that writers and journalists don't mention that possibility makes me instantly skeptical about the quality of the article I'm reading
- NitpickLawyer 2y ago
  > Many people doubt the actual performance of Grok 3 and suspect it has been specifically trained on the benchmarks 2 anecdotes here: - just before grok2 was released, they put it on livearena under a pseudonim. If you read the topics (reddit,x,etc) when that hit, everyone was raving about the model. People were saying it's the next 4o, that it's so good, hyped, so on. Then it launched, and they revealed the pseudonim, and everyone started shitting on it. There is a lot of bias in this area, especially with anything touching bad spaceman, so take "many people doubt" with a huge grain of salt. People be salty. - there are benchmarks that seem to correlate very well with end to end results on a variety of tasks. Livebench is one of them. Models scoring highly there have proven to perform well on general tasks, and don't feel like they cheated. This is supported by the finding in that paper that found models like phi and qwen to lose ~10-20% of their benchmarks scores when checked against newly-built, unseen but similar tasks. Models scoring strongly on livebench didn't see that big of a gap.
  - ttoinou 2y ago
    Interesting, thank you !
  - Mekoloto 2y ago
    I'm following AI news and models for few years now and i have not read about your Grok2 controversy. Nonetheless, i do not use grok and i do not try it out due to it being part of Musk. I'm also not aware that Grok2 was communicated as the top model in any relevant timespan at all. Perhaps it just didn't deliver? Or a lot more people are not awaare of how to use it or boycot Musk. After all he clearly doesn't care for any rules or laws it is probably a very high risk sending anything to grok.
aucisson_masque 2y ago
Last time I used chatbox arena, I was the one to ask question to LLM and so I made my own benchmark. There wasn't any predefined question. How could Musk LLM train on data that does not yet exist ?
- HenryBemis 2y ago
  That. I have used only ChatGPT and I remember asking 4 legacy to write some code. I asked o3 the same question when it came out, and then I compared the codes. o3 was 'better' more precise, more detailed, less 'crude'. Now, don't get me wrong, crude worked fine. But when I wanted to do the v1.1 and v1.2 o3 nailed it every time, while 4 legacy was simply bad and full of errors. With that said, I assume that every 'next' version of each engine is using my 'prompts' to train, so each new version has the benefit of having already processed my initial v1.0 and then v1.1 and then v1.2. So it is somewhat 'unfair' because for "ChatGTP v2024" my v1.0 is brand new while for "ChatGTP v2027" my v1.0, v1.1, v1.2 is already in the training dataset. I haven't used Grok yet, perhaps it's time to pause that OpenAI payment and give Elon some $$$ and see how it works 'for me'.
- JKCalhoun 2y ago
  That's true. You can head over to lmarena.ai and pit it against other LLMs yourself. I only tried two prompts but was surprised at how well it did. There are "leaderboards" there that provide more anecdotal data points than my two.
jiggawatts 2y ago
People have called LLMs a "blurry picture of the Internet". Improving the focus won't change the subject of the picture, it just makes it sharper. Every photographer knows this! A fundamentally new approach is needed, such as training AIs in phases, where instead of merely training them to learn to parrot their inputs, the first AI is used to critique and analyse the inputs, which is then used to train another model in a second pass, which is used to critique the data again, and so on, probably for half a dozen or more iterations. On each round, the model can learn not just what it heard, but also an analysis of the veracity, validity, and consistency. Notably, something akin to this was done for training Deepseek, but only in a limited fashion.
cardanome 2y ago
> Sabine Hossenfelder She really needs to stop commenting on topics outside of theoretical physics. Even in physics she does not represent the scientific consensus but has some very questionable fringe beliefs like labeling whole sub-fields as "scams to get funding". She regularly speaks with "scientific authority" about topics she barely knows anything about. Her video on autism is considered super harmful and misleading by actual autistic people. She also thinks she is an expert on trans-issues and climate change. And I doubt she know enough about artificial intelligence and computer science to comment on LLMs.
- Mekoloto 2y ago
  Your statement is missleading. She doesn't say she is an expert on trans-issues at all! She analyzed the studies and looked at data and stated that there is no real transpendemic but highlighed an statistical increased numbers in young woman without stating a clear opinion on this finding. The climate change videos do the same thing. She evaluates these studies discusses them to clarify that for her, certain numbers are unspecific and she also is not coming to a clear conclusion in sense of climate change yes, no, bad, good. She is for sure not an expert in all fields, but her way of discussing these topics are based on studies, numbers and is a good viewpoint. The funding scam you mention is a reference of "these people get billions for particle research but the outcome for us as society is way to small"
  - cardanome 2y ago
    Having studied physics does not allow you to evaluate studies in completely unrelated field in any meaningful way. Especially not in such politically-charged fields that require deeper knowledge about the historical context, the different interest groups and their biases and so on. Her video on trans-issues labels people that advocate for the rights of trans-people as "extremists" and presents transphobic talking points as valid part of the scientific discussion. Her trying to appear "neutral" and "just presenting the science" is exactly the issue. Using her authority as a scientist when talking about topics she has no expertise in. Here is a debunking of her video on trans-issues: https://www.youtube.com/watch?v=r6Kau7bO3Fw https://www.youtube.com/watch?v=r6Kau7bO3Fw Here is a longer criticism of her video on autism: https://www.youtube.com/watch?v=vaZZiX0veFY https://www.youtube.com/watch?v=vaZZiX0veFY
aubanel 2y ago
> Which shows that "massive scaling", even enormous, gigantic scaling, doesn't improve intelligence one bit; it improves scope, maybe, or flexibility, or coverage, or something, but not "intelligence". Do you have any data to support 1. That grok is not more intelligent than previous models (you gave one anecdotal datapoint), and 2. That it was trained on more data than other models like o1 and Claude-3.5 Sonnet? All datapoints I have support the opposite: scaling actually increases intelligence of models. (agreed, calling this "intelligence" might be a stretch, but alternative definitions like "scope, maybe, or flexibility, or coverage, or something" seem to me like beating around the bush to avoid saying that machines have intelligence) Check out the technical report of Llama 3 for instance, with nice figures on how scaling up model training does increase performance on intelligence tests (might as well call that intelligence): https://arxiv.org/abs/2407.21783 https://arxiv.org/abs/2407.21783
BiteCode_dev 2y ago
It is very up to date however, I asked it about recent stuff on python packaging, and it gets it while others don't.

by bambax 2y ago

melodyogonna 2y ago

How can it be specifically trained on benchmarks when it is leading on blind chatbot tests? The post you quoted is not a Grok problem if other LLMs are also failing, it seems, to me, to be a fundamental failure in the current approach to AI model development.

bearjaws 2y ago
Any LLM that is uncensored does well on Chatbot tests because a refusal is an automatic loss. And since 30% of people using Chatbots are Gooning it up theres far more refusals...
- pyinstallwoes 2y ago
  Gooning?
  - bearjaws 2y ago
    https://www.urbandictionary.com/define.php?term=gooning https://www.urbandictionary.com/define.php?term=gooning
nycdatasci 2y ago
I think a more plausible path to gaming benchmarks would be to use watermarks in text output to identify your model, then unleash bots to consistently rank your model over opponents.

ttoinou 2y ago

Many people doubt the actual performance of Grok 3 and suspect it has been specifically trained on the benchmarks That's something I always wondered about, Goodhart's law is so obvious to apply to each new AI release. Even the fact that writers and journalists don't mention that possibility makes me instantly skeptical about the quality of the article I'm reading

NitpickLawyer 2y ago
> Many people doubt the actual performance of Grok 3 and suspect it has been specifically trained on the benchmarks 2 anecdotes here: - just before grok2 was released, they put it on livearena under a pseudonim. If you read the topics (reddit,x,etc) when that hit, everyone was raving about the model. People were saying it's the next 4o, that it's so good, hyped, so on. Then it launched, and they revealed the pseudonim, and everyone started shitting on it. There is a lot of bias in this area, especially with anything touching bad spaceman, so take "many people doubt" with a huge grain of salt. People be salty. - there are benchmarks that seem to correlate very well with end to end results on a variety of tasks. Livebench is one of them. Models scoring highly there have proven to perform well on general tasks, and don't feel like they cheated. This is supported by the finding in that paper that found models like phi and qwen to lose ~10-20% of their benchmarks scores when checked against newly-built, unseen but similar tasks. Models scoring strongly on livebench didn't see that big of a gap.
- ttoinou 2y ago
  Interesting, thank you !
- Mekoloto 2y ago
  I'm following AI news and models for few years now and i have not read about your Grok2 controversy. Nonetheless, i do not use grok and i do not try it out due to it being part of Musk. I'm also not aware that Grok2 was communicated as the top model in any relevant timespan at all. Perhaps it just didn't deliver? Or a lot more people are not awaare of how to use it or boycot Musk. After all he clearly doesn't care for any rules or laws it is probably a very high risk sending anything to grok.

aucisson_masque 2y ago

Last time I used chatbox arena, I was the one to ask question to LLM and so I made my own benchmark. There wasn't any predefined question. How could Musk LLM train on data that does not yet exist ?

HenryBemis 2y ago
That. I have used only ChatGPT and I remember asking 4 legacy to write some code. I asked o3 the same question when it came out, and then I compared the codes. o3 was 'better' more precise, more detailed, less 'crude'. Now, don't get me wrong, crude worked fine. But when I wanted to do the v1.1 and v1.2 o3 nailed it every time, while 4 legacy was simply bad and full of errors. With that said, I assume that every 'next' version of each engine is using my 'prompts' to train, so each new version has the benefit of having already processed my initial v1.0 and then v1.1 and then v1.2. So it is somewhat 'unfair' because for "ChatGTP v2024" my v1.0 is brand new while for "ChatGTP v2027" my v1.0, v1.1, v1.2 is already in the training dataset. I haven't used Grok yet, perhaps it's time to pause that OpenAI payment and give Elon some $$$ and see how it works 'for me'.
JKCalhoun 2y ago
That's true. You can head over to lmarena.ai and pit it against other LLMs yourself. I only tried two prompts but was surprised at how well it did. There are "leaderboards" there that provide more anecdotal data points than my two.

jiggawatts 2y ago

People have called LLMs a "blurry picture of the Internet". Improving the focus won't change the subject of the picture, it just makes it sharper. Every photographer knows this! A fundamentally new approach is needed, such as training AIs in phases, where instead of merely training them to learn to parrot their inputs, the first AI is used to critique and analyse the inputs, which is then used to train another model in a second pass, which is used to critique the data again, and so on, probably for half a dozen or more iterations. On each round, the model can learn not just what it heard, but also an analysis of the veracity, validity, and consistency. Notably, something akin to this was done for training Deepseek, but only in a limited fashion.

cardanome 2y ago

> Sabine Hossenfelder She really needs to stop commenting on topics outside of theoretical physics. Even in physics she does not represent the scientific consensus but has some very questionable fringe beliefs like labeling whole sub-fields as "scams to get funding". She regularly speaks with "scientific authority" about topics she barely knows anything about. Her video on autism is considered super harmful and misleading by actual autistic people. She also thinks she is an expert on trans-issues and climate change. And I doubt she know enough about artificial intelligence and computer science to comment on LLMs.

Mekoloto 2y ago
Your statement is missleading. She doesn't say she is an expert on trans-issues at all! She analyzed the studies and looked at data and stated that there is no real transpendemic but highlighed an statistical increased numbers in young woman without stating a clear opinion on this finding. The climate change videos do the same thing. She evaluates these studies discusses them to clarify that for her, certain numbers are unspecific and she also is not coming to a clear conclusion in sense of climate change yes, no, bad, good. She is for sure not an expert in all fields, but her way of discussing these topics are based on studies, numbers and is a good viewpoint. The funding scam you mention is a reference of "these people get billions for particle research but the outcome for us as society is way to small"
- cardanome 2y ago
  Having studied physics does not allow you to evaluate studies in completely unrelated field in any meaningful way. Especially not in such politically-charged fields that require deeper knowledge about the historical context, the different interest groups and their biases and so on. Her video on trans-issues labels people that advocate for the rights of trans-people as "extremists" and presents transphobic talking points as valid part of the scientific discussion. Her trying to appear "neutral" and "just presenting the science" is exactly the issue. Using her authority as a scientist when talking about topics she has no expertise in. Here is a debunking of her video on trans-issues: https://www.youtube.com/watch?v=r6Kau7bO3Fw https://www.youtube.com/watch?v=r6Kau7bO3Fw Here is a longer criticism of her video on autism: https://www.youtube.com/watch?v=vaZZiX0veFY https://www.youtube.com/watch?v=vaZZiX0veFY

aubanel 2y ago

> Which shows that "massive scaling", even enormous, gigantic scaling, doesn't improve intelligence one bit; it improves scope, maybe, or flexibility, or coverage, or something, but not "intelligence". Do you have any data to support 1. That grok is not more intelligent than previous models (you gave one anecdotal datapoint), and 2. That it was trained on more data than other models like o1 and Claude-3.5 Sonnet? All datapoints I have support the opposite: scaling actually increases intelligence of models. (agreed, calling this "intelligence" might be a stretch, but alternative definitions like "scope, maybe, or flexibility, or coverage, or something" seem to me like beating around the bush to avoid saying that machines have intelligence) Check out the technical report of Llama 3 for instance, with nice figures on how scaling up model training does increase performance on intelligence tests (might as well call that intelligence): https://arxiv.org/abs/2407.21783 https://arxiv.org/abs/2407.21783

BiteCode_dev 2y ago

It is very up to date however, I asked it about recent stuff on python packaging, and it gets it while others don't.