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leprechaun1066 | 1 year ago

Calling them hallucinations was a huge mistake.

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gwervc|1 year ago

It is a good branding, like neural networks, and even artificial intelligence was. The good point is it makes really easy to detect who is a bullshiter and who understand at least very remotely what a LLM is supposed to produce.

JKCalhoun|1 year ago

I won't defend the term but am curious what you think would have been also concise but more accurate. Calling them for example "inevitable statistical misdirections" doesn't really roll off the tongue.

goatlover|1 year ago

Confabulation, if the desire is to use a more apt psychological analogy.

malfist|1 year ago

It's a bug. Any other system where you put in one input and expect a certain output and get something else it'd be called a bug. Making up new terms for AI doesn't help.

threeseed|1 year ago

I see two types of faults with LLMs.

a) They output incorrect results given a constrained set of allowable outputs.

b) When unconstrained they invent new outputs unrelated to what is being asked.

So for me the term hallucination accurately describes b) e.g. you ask for code to solve a problem and it invents new APIs that don't exist. Technically it is all just tokens and probabilities but it's a reasonably term to describe end user behaviour.

sfink|1 year ago

The term is actually fine. The problem is when it's divorced from the reality of:

> in some sense, hallucination is all LLMs do. They are dream machines.

If you understand that, then the term "hallucination" makes perfect sense.

Note that this in no way invalidates your point, because the term is constantly used and understood without this context. We would have avoided a lot of confusion if we had based it on the phrase "make shit up" and called it "shit" from the start. Marketing trumps accuracy again...

(Also note that I am not using shit in a pejorative sense here. Making shit up is exactly what they're for, and what we want them to do. They come up with a lot of really good shit.)

jebarker|1 year ago

I agree with your point, but I don't think anthropomorphizing LLMs is helpful. They're statistical estimators trained by curve fitting. All generations are equally valid for the training data, objective and architecture. To me it's much clearer to think about it that way versus crude analogies to human brains.