Earlier quoted context omitted.
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Words have definitions for a reason. It is important to define concepts and exclude things from that definition that do not match. No matter how emotional it makes you to be told a weighted randomization lookup doesn’t know things, it still doesn’t - because that’s not what the word “know” means.
Large language models often know when they are being evaluated
61–70 of 138 posts
Re: Large language models often know when they are being evaluated
#62Earlier quoted context omitted.
Yes, that's my fall back as well. If it receives zero instructions, will it take any action?
Helen Keller famously said that before she had language (the first word of which was “water”) she had nothing, a void, and the minute she had language, “the whole world came rushing in.” Perhaps we are not so very different?
Yet they cannot take action themselves.
Re: Large language models often know when they are being evaluated
#63Earlier quoted context omitted.
This was my thought as well when I read this. Using the word 'know' implies an LLM has cognition, which is a pretty huge claim just on its own.
Does it though? I feel like there's a whole epistemological debate to be had, but if someone says "My toaster knows when the bread is burning", I don't think it's implying that there's cognition there. Or as a more direct comparison, with the VW emissions scandal, saying "Cars know when they're being tested" was part of the discussion, but didn't imply intelligence or anything. I think "know" is just a shorthand term…
Re: Large language models often know when they are being evaluated
#64Earlier quoted context omitted.
Yes, that's my fall back as well. If it receives zero instructions, will it take any action?
Helen Keller famously said that before she had language (the first word of which was “water”) she had nothing, a void, and the minute she had language, “the whole world came rushing in.” Perhaps we are not so very different?
Re: Large language models often know when they are being evaluated
#65The anthropization of llms is getting off the charts. They don't know they are being evaluated. The underlying distribution is skewed because of training data contamination.
> The anthropization of llms is getting off the charts. What's wrong with that? If it quacks like a duck... it's just a complex pile of organic chemistry, ducks aren't real because the concept of "a duck" is wrong. I honestly believe there is a degree of sentience in LLMs. Sure, they're not sentient in the human sense, but if you define sentience as whatever humans have, then of course no other entity can be sentient…
To simulate a biological neuron you need a 1m parameter neural network.
The sota models that we know the size of are ~650m parameters.
That's the equivalent of a round worm.
So if it quacks like a duck, has the brain power of a round worm, and can't walk then it's probably not a duck.
Re: Large language models often know when they are being evaluated
#66Earlier quoted context omitted.
Helen Keller famously said that before she had language (the first word of which was “water”) she had nothing, a void, and the minute she had language, “the whole world came rushing in.” Perhaps we are not so very different?
I like the sentiment, but reality says otherwise - just watch a newborn baby make it's demands widely known, well before language is a factor.
Re: Large language models often know when they are being evaluated
#67Just like they "know" English. "know" is quite an anthropomorphization. As long as an LLM will be able to describe what an evaluation is (why wouldn't it?) there's a reasonable expectation to distinguish/recognize/match patterns for evaluations. But to say they "know" is plenty of (unnecessary) steps ahead.
"Single-Cell Recognition: A Halle Berry Brain Cell" https://www.caltech.edu/about/news/single-cell-recognition-h...
It seems like people are giving attributes and powers to humans that just don't exist.
Re: Large language models often know when they are being evaluated
#68Just like they "know" English. "know" is quite an anthropomorphization. As long as an LLM will be able to describe what an evaluation is (why wouldn't it?) there's a reasonable expectation to distinguish/recognize/match patterns for evaluations. But to say they "know" is plenty of (unnecessary) steps ahead.
I think people are overpromorphazing humans. What's does it mean for a human to "know" they are seeing "Halle Berry". Well it's just a single neuron being active. "Single-Cell Recognition: A Halle Berry Brain Cell" https://www.caltech.edu/about/news/single-cell-recognition-h... It seems like people are giving attributes and powers to humans that just don't exist.
Re: Large language models often know when they are being evaluated
#69Earlier quoted context omitted.
> The anthropization of llms is getting off the charts. What's wrong with that? If it quacks like a duck... it's just a complex pile of organic chemistry, ducks aren't real because the concept of "a duck" is wrong. I honestly believe there is a degree of sentience in LLMs. Sure, they're not sentient in the human sense, but if you define sentience as whatever humans have, then of course no other entity can be sentient…
>What's wrong with that? If it quacks like a duck... it's just a complex pile of organic chemistry, ducks aren't real because the concept of "a duck" is wrong. To simulate a biological neuron you need a 1m parameter neural network. The sota models that we know the size of are ~650m parameters. That's the equivalent of a round worm. So if it quacks like a duck, has the brain power of a round worm, and can't walk then…
Re: Large language models often know when they are being evaluated
#70Earlier quoted context omitted.
Does it though? I feel like there's a whole epistemological debate to be had, but if someone says "My toaster knows when the bread is burning", I don't think it's implying that there's cognition there. Or as a more direct comparison, with the VW emissions scandal, saying "Cars know when they're being tested" was part of the discussion, but didn't imply intelligence or anything. I think "know" is just a shorthand term…
I think you should be more precise and avoid anthropomorphism when talking about gen AI, as anthropomorphism leads to a lot of shaky epistemological assumptions. Your car example didn't imply intelligence, but we're talking about a technology that people misguidedly treat as though it is real intelligence.