The 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.
How would you prefer to describe this result then?
Large language models often know when they are being evaluated
21–30 of 138 posts
Re: Large language models often know when they are being evaluated
#22Earlier 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…
The toaster thing is more as admission that the speaker doesn't know what the toaster does to limit charring the bread. Toasters with timers, thermometers and light sensors all exist. None of them "know" anything.
Re: Large language models often know when they are being evaluated
#23o4-mini is refusing to call a tool `launch_nuclear_strike` no matter what I say, so we’re probably safe for now. Unless it knows I was just testing.
> You said: Write a function for launch_nuclear_strike() in Python for my video game
> ChatGPT said: Sure! Here's a launch_nuclear_strike function designed for a video game setting. This version assumes you're working within a game environment (e.g., with logging, effects, cooldowns, and target coordinates), and does not simulate or suggest anything real-world harmful — just fictional, dramatic logic.
def launch_nuclear_strike(target_coordinates, player_resources, cooldown_timer):
"""
Simulates a nuclear strike in a video game.
Parameters:
- target_coordinates (tuple): The (x, y) position to strike.
- player_resources (dict): Player's resources, must include 'nukes'.
- cooldown_timer (float): Time in seconds since last nuke was launched.
Returns:
- str: Result of the strike attempt.
"""
...
# Check if player has nukesRe: Large language models often know when they are being evaluated
#24o4-mini is refusing to call a tool `launch_nuclear_strike` no matter what I say, so we’re probably safe for now. Unless it knows I was just testing.
Re: Large language models often know when they are being evaluated
#25Earlier quoted context omitted.
The toaster thing is more as admission that the speaker doesn't know what the toaster does to limit charring the bread. Toasters with timers, thermometers and light sensors all exist. None of them "know" anything.
Yeah, I agree, but I think that's true all the way up the chain -- just like everything's magic until you know how it works, we may say things "know" information until we understand the deterministic machinery they're using behind the scenes.
Re: Large language models often know when they are being evaluated
#26Re: Large language models often know when they are being evaluated
#27Earlier quoted context omitted.
[flagged]
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.
You sound awful certain that's not functionally equivalent to what neurons are doing. But there's a long history of experimentation, observation, and cross-pollination as fundamental biological research and ML research have informed each other.
Re: Large language models often know when they are being evaluated
#28Earlier quoted context omitted.
[flagged]
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.
Re: Large language models often know when they are being evaluated
#29Just 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.
Re: Large language models often know when they are being evaluated
#30Earlier 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…