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>Pointing out the failures of their favorite LLM to prove to them that it's not doing what they think it's doing just falls on deaf ears It's not falling on de

>Pointing out the failures of their favorite LLM to prove to them that it's not doing what they think it's doing just falls on deaf ears It's not falling on deaf ears. It's because it's stupidity to think the "failures" are proof. Should I point out all the failures in human intelligence? Humans fuck up all the time. Humans make stupid mistakes, assumptions, biases, errors in reasoning and leaps in logic all the time. According to your logic that's proof that humans don't understand anything.

worrycue 4y ago
> According to your logic that's proof that humans don't understand anything. Well, it's reasonable to conclude that the people who constantly making "stupid mistakes, assumptions, and biases leaps in logic" don't actually know what they are talking about.
- HervalFreire 4y ago
  Right but can you make the claim that they don't understand anything at all? That they're stochastic parrots? Because that's the direction where your own logic is taking you. I'll concede one thing, LLMs can't use vicious insults and subtle slights to cover up the lack of a good argument. That's a human specialty done by people who are too scared to admit that they're wrong.
  - worrycue 4y ago
    > claim that they don't understand anything at all? They certainly don't understand enough to get things right without a few thousand examples already demonstrating the solution to a specific problem.
    - HervalFreire 4y ago
      Taking a long time to learn something doesn't mean that something wasn't learned nor understood. Two separate topics. Even chatGPT understands this: ME: Does someone or something requiring a thousand examples to understand something imply that it doesn't understand anything? CHATGPT: No, requiring a thousand examples to understand something does not necessarily imply that someone or something doesn't understand anything. It simply suggests that the thing being understood is complex and requires a significant amount of data or examples to fully grasp. CHATGPT: For example, in the context of machine learning, a large dataset of examples may be required to train a model to perform a specific task accurately. This does not mean that the model doesn't understand anything; rather, it suggests that it needs a lot of data to be able to generalize and apply its understanding to new situations. CHATGPT: Similarly, in the case of human learning, some concepts or ideas may be challenging to understand without multiple examples or explanations. This does not mean that the person doesn't understand anything; it simply suggests that they need more exposure to the concept to fully comprehend it. CHATGPT: In summary, the amount of data or examples required to understand something does not necessarily indicate a lack of understanding. It may simply reflect the complexity of the topic or the learning process. Why don't you understand it?
      - inkysigma 4y ago
        I have to disagree with that. Like maybe for a toy example to demonstrate what I'm talking about, imagine I was teaching you the addition operation mod 100 and I gave a description of the operation f(x, y) = x + y % 100 for x, y in Z_100. If you take more than 100^2 samples to learn the function, I'm not sure you understand the function. Obviously, in that many samples, you could've just specified a look up table without understanding what each operation is doing or what the underlying domain is. Part of why sample efficiency is interesting is that humans have high sample efficiency since they somehow perform reasoning and this generalizes well to some pretty abstract spaces. As someone who's worked with ML models, I'm genuinely envious of the generalization capabilities of humans and I think it's something that researchers are going to have to work on. I'm pretty sure there's still a lot of skepticism in academia that scale is everything needed to achieve better models and that we're still missing lots of things. Some of my skepticism around claims of LLMs reasoning or performing human like things is that they really appear to not generalize well. Lots of the incredible examples people have shown are very slightly out of the bounds of the internet. When you start asking it for hard logic or to really synthesize something novel outside the domain of the internet, it rapidly begins to fail seemingly in proportion to the amount of knowledge the internet may have on it. How might we differentiate being a really good soft/fuzzy lookup table of the internet that is able to fuzzily mix language together from genuine knowledge and generalization. This might just be a testament to the sheer scope and size of the internet in how much apparent capabilities GPT has. This isn't to say they cannot be useful ever, a lot of work is derivative, but I think there's a large portion of the claim that it's understanding things that's unwarranted. Last I checked, chatGPT was giving wrong answers for the sums of very large numbers which is unusual if it understands addition.
        HervalFreire 4y ago
        You're describing over fitting to some look up table. Can't be what's happening here. Because the examples LLMs are answering are well out of bounds of the "100^2" training data. The internet is huge but it's not that huge. One can easily find chatGPT saying, doing or creating things that obviously come from a generalized model. It's actually trivial to find examples of chatGPT answering questions with responses that are wholly unique and distinct from the training data, as in the answer it gave you could not have existed anywhere on the internet. Clearly humans don't need that much training data. We can form generalizations from a much smaller sample size. This does not indicate that for machine learning a generalization doesn't exist in LLMs when clearly the answers demonstrate that it does.
  - deleted 4y ago
    [deleted]
  - RHSman2 4y ago
    We learn that skill in our training. We are highly trained LLM’s skilled in the nuances. Everyone is acting like LLM model is the finished product. It is a baby. Do you remember your nuanced language capability at 5, 8, 10, 20? Are you the finished product? Do you still make massive leaps of awareness? Yes.
Lewton 4y ago
Failures are proof, successes are not, a broken clock is right twice a day after all
- HervalFreire 4y ago
  So if a human fails at something it's proof that the human doesn't understand anything and that a human is a stochastic parrot? I think your clock analogy doesn't fit. A car with a broken mirror still runs.
  - Lewton 4y ago
    The output of GPT is “random” in a sense that output from humans are not. I can ask it logic puzzles and sometimes it’ll get the logic puzzle right by chance, and other times it won’t. I can’t use the times it gets the logic puzzle right, as evidence that it understood the puzzle. All of these blog posts that are popping up suffer from survivor bias, nobody is sharing blog posts of GPTs failures
    - HervalFreire 4y ago
      > I can’t use the times it gets the logic puzzle right, as evidence that it understood the puzzle. No this is bias from your end. It really depends on the puzzle. You need to give it a puzzle with trillions of possible answers. In this case if it gets a right answer even once the probability is so low for this to happen by chance that it means an aspect of the model understands the concept; While another aspect of the model doesn't understand it. It's possible for even humans to have contradictory thinking and desires. Therefore a claim cannot be made that it understands nothing.

worrycue 4y ago

> According to your logic that's proof that humans don't understand anything. Well, it's reasonable to conclude that the people who constantly making "stupid mistakes, assumptions, and biases leaps in logic" don't actually know what they are talking about.

HervalFreire 4y ago
Right but can you make the claim that they don't understand anything at all? That they're stochastic parrots? Because that's the direction where your own logic is taking you. I'll concede one thing, LLMs can't use vicious insults and subtle slights to cover up the lack of a good argument. That's a human specialty done by people who are too scared to admit that they're wrong.
- worrycue 4y ago
  > claim that they don't understand anything at all? They certainly don't understand enough to get things right without a few thousand examples already demonstrating the solution to a specific problem.
  - HervalFreire 4y ago
    Taking a long time to learn something doesn't mean that something wasn't learned nor understood. Two separate topics. Even chatGPT understands this: ME: Does someone or something requiring a thousand examples to understand something imply that it doesn't understand anything? CHATGPT: No, requiring a thousand examples to understand something does not necessarily imply that someone or something doesn't understand anything. It simply suggests that the thing being understood is complex and requires a significant amount of data or examples to fully grasp. CHATGPT: For example, in the context of machine learning, a large dataset of examples may be required to train a model to perform a specific task accurately. This does not mean that the model doesn't understand anything; rather, it suggests that it needs a lot of data to be able to generalize and apply its understanding to new situations. CHATGPT: Similarly, in the case of human learning, some concepts or ideas may be challenging to understand without multiple examples or explanations. This does not mean that the person doesn't understand anything; it simply suggests that they need more exposure to the concept to fully comprehend it. CHATGPT: In summary, the amount of data or examples required to understand something does not necessarily indicate a lack of understanding. It may simply reflect the complexity of the topic or the learning process. Why don't you understand it?
    - inkysigma 4y ago
      I have to disagree with that. Like maybe for a toy example to demonstrate what I'm talking about, imagine I was teaching you the addition operation mod 100 and I gave a description of the operation f(x, y) = x + y % 100 for x, y in Z_100. If you take more than 100^2 samples to learn the function, I'm not sure you understand the function. Obviously, in that many samples, you could've just specified a look up table without understanding what each operation is doing or what the underlying domain is. Part of why sample efficiency is interesting is that humans have high sample efficiency since they somehow perform reasoning and this generalizes well to some pretty abstract spaces. As someone who's worked with ML models, I'm genuinely envious of the generalization capabilities of humans and I think it's something that researchers are going to have to work on. I'm pretty sure there's still a lot of skepticism in academia that scale is everything needed to achieve better models and that we're still missing lots of things. Some of my skepticism around claims of LLMs reasoning or performing human like things is that they really appear to not generalize well. Lots of the incredible examples people have shown are very slightly out of the bounds of the internet. When you start asking it for hard logic or to really synthesize something novel outside the domain of the internet, it rapidly begins to fail seemingly in proportion to the amount of knowledge the internet may have on it. How might we differentiate being a really good soft/fuzzy lookup table of the internet that is able to fuzzily mix language together from genuine knowledge and generalization. This might just be a testament to the sheer scope and size of the internet in how much apparent capabilities GPT has. This isn't to say they cannot be useful ever, a lot of work is derivative, but I think there's a large portion of the claim that it's understanding things that's unwarranted. Last I checked, chatGPT was giving wrong answers for the sums of very large numbers which is unusual if it understands addition.
      - HervalFreire 4y ago
        You're describing over fitting to some look up table. Can't be what's happening here. Because the examples LLMs are answering are well out of bounds of the "100^2" training data. The internet is huge but it's not that huge. One can easily find chatGPT saying, doing or creating things that obviously come from a generalized model. It's actually trivial to find examples of chatGPT answering questions with responses that are wholly unique and distinct from the training data, as in the answer it gave you could not have existed anywhere on the internet. Clearly humans don't need that much training data. We can form generalizations from a much smaller sample size. This does not indicate that for machine learning a generalization doesn't exist in LLMs when clearly the answers demonstrate that it does.
- deleted 4y ago
  [deleted]
- RHSman2 4y ago
  We learn that skill in our training. We are highly trained LLM’s skilled in the nuances. Everyone is acting like LLM model is the finished product. It is a baby. Do you remember your nuanced language capability at 5, 8, 10, 20? Are you the finished product? Do you still make massive leaps of awareness? Yes.

Lewton 4y ago

Failures are proof, successes are not, a broken clock is right twice a day after all

HervalFreire 4y ago
So if a human fails at something it's proof that the human doesn't understand anything and that a human is a stochastic parrot? I think your clock analogy doesn't fit. A car with a broken mirror still runs.
- Lewton 4y ago
  The output of GPT is “random” in a sense that output from humans are not. I can ask it logic puzzles and sometimes it’ll get the logic puzzle right by chance, and other times it won’t. I can’t use the times it gets the logic puzzle right, as evidence that it understood the puzzle. All of these blog posts that are popping up suffer from survivor bias, nobody is sharing blog posts of GPTs failures
  - HervalFreire 4y ago
    > I can’t use the times it gets the logic puzzle right, as evidence that it understood the puzzle. No this is bias from your end. It really depends on the puzzle. You need to give it a puzzle with trillions of possible answers. In this case if it gets a right answer even once the probability is so low for this to happen by chance that it means an aspect of the model understands the concept; While another aspect of the model doesn't understand it. It's possible for even humans to have contradictory thinking and desires. Therefore a claim cannot be made that it understands nothing.