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Grok 3.0 apparently can listen to music and integrate the results with the rest of its knowledge.
Twitter released their newest iteration on AI, it's fun, clever, and noticeably less lobotomized than basically everything before it. It's currently free with a basic twitter account. But I accidentally discovered a thing that kinda blew my mind. I got into a silly argument with it, as one does, about whether you can legitimately pronounce "gambler" with three syllables. As one of my arguments I brought up a Johnny Cash song where he, to my ear, does it. The robot responded:
I disagree obviously, but notice the timestamp!!! I'm reasonably sure that nobody in the history of the internet had this exact argument before and mentioned the exact timestamp in that exact song. Moreover, before that I asked it about "House of the Raising Sun" (because I misremembered the vocalist drawling "gambling" there) and the robot also timestamped the place in the recording where it was said.
So I don't know. It's possible that this is a result of an unsophisticated hack, give the AI a database of timestamped subtitles for youtube videos (something they already have generated), then it bullshits its way through the argument about what was actually said and how. That's totally possible, it's really good at bullshitting!
The other possibility is that it actually listens to videos/audios and analyses them on the fly or during training, whatever. What's super interesting about is that, look, we started with LLMs that literally had not a single real world reference, nothing that could remotely qualify as a qualia of say seeing an apple. They were trained entirely on people talking about their perceptions of apples, and somehow they managed to learn what apples are pretty well, without ever seeing one (which all philosophers agreed should be impossible, seeing apples must come first, and yet here we were). And now, if it's not just a subtitle hack, then we have quietly passed another milestone, the robots now can hear and see and correlate that with their knowledge bases.
Also, I asked the robot directly:
(timestamped subtitles followed)
Idk, it responded pretty much instantly, so it could be lying. Or maybe it has preprocessed subtitles for popular videos.
I don't know exactly what's is going on here but LLMs often respond like that. I'm not sure that "lying" is the correct term or if it's more accurate to say that they frequently don't "perceive themselves" as having the literal knowledge that they're trained on and with some prompting can reproduce.
IMO this is roughly the right way to think about it. LLMs probably don't even have the capability to know what they know; it's just not what they're trained to do. A lot of people confuse the LLM's simulation of a chatbot with the LLM itself, but they're not the same. (e.g. we can't settle the question of whether an LLM is conscious by asking it "are you conscious?". The answer will just depend on what it thinks the chatbot would say.) From the LLM's perspective it's perfectly reasonable to extend a conversation with "the answer is" even when the word after that is undetermined. Hence hallucinations.
(I think RLHF helps a bit with this, allowing it to recognize "hard questions" that it's likely to get wrong, but that's not the same as introspection.)
RLHF tends to make a model less calibrated. Substantially so.
By "calibration" I assume you mean having low confidence when it's wrong. It's counter-intuitive to me, but some quick Googling suggests that you're right about that. Good correction. I guess that's part of why fixing hallucinations has proven so intractable so far.
It's worse than you think.
Look at figure 8 of the GPT4 'technical report'. Or figure 9 of this paper on mode collapse. It's all across the calibration scale that gets messed up, not just the low extreme.
If you, say, ask a LLM for the result of a fair d4 roll (1-4), with sufficient formatting/etc such that with overwhelming probability it will output just the tokens '1', '2', '3', or '4', a properly calibrated model "should" result in the following output probabilities:
'1': 0.25 '2': 0.25 '3': 0.25 '4': 0.25And many base models are pretty close. Not perfect, but reasonable.
With RLHF, however, you'll often see something like, say:
'1': 0.02 '2': 0.07 '3': 0.90 '4': 0.01Why? Short answer:
Consider the case of a slightly weighted coin tossed once per training session, that flips heads 60% of the time. A base model will result in the highest training score if it flips heads 60% of the time.
But a RLHF'd model will result in the highest training score if it flips heads 100% of the time. Because when the user sees "which is a more likely answer: heads or tails" - they will answer "heads". And so the model will be trained to answer "heads".
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