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I like this but at the same time it seems tricky don't you think? Is the AI model intuiting your intent? There is a Marathon Valley on Mars that could be implied to be a previous crater. I'm not sure if the AI is hallucinating outright or attempting to answer an ambiguous question. It's like saying "tell me about the trade building in New York". Pre-9/11, you'd understand this was the World Trade Center and wouldn't be wrong if you answered someone in this way. "Tell me about the Triangle statue". "Oh the Triangle statue was built in ancient egypt around BC 3100". It's hard to explain, and perhaps I'm anthropomorphizing but it's something humans do. Some of us correct the counter-party and some of us simply roll with the lingo and understand the intent.

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thatjoeoverthr a day ago | parent | next [-]

It’s a roll of the dice whether it’s on Mars, Greece or elsewhere. It just says stuff!

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krainboltgreene 20 hours ago | parent | prev [-]

> Is the AI model intuiting your intent?

I keep seeing this kind of wording and I wonder: Do you know how LLM's work? Not trying to be catty, actually curious where you sit.

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dudeinhawaii 4 hours ago | parent | next [-]

Yes, I understand the basics. LLMs predict the next most probable tokens based on patterns in their training data and the prompt context. For the 'Marathon crater' example, the model doesn't have a concept of 'knowing' versus 'not knowing' in our sense. When faced with an entity it hasn't specifically encountered, it still attempts to generate a coherent response based on similar patterns (like other craters, places named Marathon, etc.).

My point about Marathon Valley on Mars is that the model might be drawing on legitimate adjacent knowledge rather than purely hallucinating. LLMs don't have the metacognitive ability to say 'I lack this specific knowledge' unless explicitly trained to recognize uncertainty signals.

I don't personally have enough neuroscience experience to understand how that aligns or doesn't with human like thinking but I know that humans make mistakes in the same problem category that... to an external observer.. are indistinguishable from "making shit up". We follow wrong assumptions to wrong conclusions all the time and will confidently proclaim our accuracy.

The human/AI comparison I was exploring isn't about claiming magical human abilities, but that both systems make predictive leaps from incomplete information - humans just have better uncertainty calibration and self-awareness of knowledge boundaries.

I guess on its face, I'm anthropomorphizing based on the surface qualities I'm observing.

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krainboltgreene 4 hours ago | parent [-]

Okay but by your own understanding it's not drawing on knowledge. It's drawing on probable similarity in association space. If you understand that then nothing here should be confusing, it's all just most probable values.

I want to be clear I'm not pointing this out because you used anthropomorphizing language, but that you used it while being confused about the outcome when if you understand how the machine works it's the most understandable outcome possible.

	▲	dudeinhawaii 4 hours ago \| parent [-]
		That's a fair point. What I find interesting (and perhaps didn't articulate properly) isn't confusion about the LLM's behavior, but the question of whether human cognition might operate on similar principles at a fundamental level - just via different mechanisms and with better calibration (similar algorithm, different substrate), which is why I used human examples at the start. When I see an LLM confidently generate an answer about a non-existent thing by associating related concepts, I wonder how different is this from humans confidently filling knowledge gaps with our own probability-based assumptions? We do this constantly - connecting dots based on pattern recognition and making statistical leaps between concepts. If we understand how human minds worked in their entirety, then I'd be more likely to say "ha, stupid LLM, it hallucinates instead of saying I don't know". But, I don't know, I see a strong similarity to many humans. What are weight and biases but our own heavy-weight neural "nodes" built up over a lifetime to say "this is likely to be true because of past experiences"? I say this with only hobbyist understanding of neural science topics mind you.

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ipaddr 19 hours ago | parent | prev [-]

How do they work? My understanding is each 5 characters are tokenized and assigned a number. If you take gpt2 it has 768 embedded dimensional values which get broken into 64 which creates 12 planes. When training starts random values are assigned to the dimensional values (never 0). Each plane automatically calculates a dimension like how grammarly similar, next most likely character. But it does this automatically based on feedback from other planes. That's where I get lost. Can you help fill in the pieces?