> It's just ironic how human-like the flaws of the system are. (Hallucinations that are asserting untrue facts, just because they are plausible from a pattern matching POV)
I think most human mistakes are different - not applying a lot of complex logic to come to an incorrect deduction/guess (= LLM hallucination), but rather just shallow recall/guess. e.g. An LLM would guess/hallucinate a capital city by using rules it had learnt about other capital cities - must be famous, large, perhaps have an airport, etc, etc; a human might just use "famous" to guess, or maybe just throw out the name of the only city they can associate to some country/state.
The human would often be aware that they are just guessing, maybe based on not remembering where/how they had learnt this "fact", but to the LLM it's all just statistics and it has no episodic memory (or even coherent training data - it's all sliced and diced into shortish context-length samples) to ground what it knows or does not know.
The reason the LLMs are of any use to anyone right now (and real people are using them for real things right now- see the millions of ChatGPT users) is because the qualitative difference between text created by a real human using a guessing heuristic vs. an LLM using statistics is qualitatively the same. Even for things that some subjectively deem "creative".
The entropy of communication also makes it so that we mostly won't ever know when a person is guessing or if they think they're telling the truth. In that sense it makes less difference to the receiver of the information what the intent was- even if it came from a human, that human's guessing/ BS level is still unknown to you, the recipient.
This difference will continue to get smaller and more imperceptible. When it will stop changing or at what rate it will change is anyone's guess.
LLMs are just trying to mimic (predict) human output, and can obviously do a great job, which is why they are useful.
I was just referring to when LLMs fail, which can be in non-human ways, not only the way in which they hallucinate, but also when they generate output that has the "shape" of something in the training set, but is nonsense.
I think most human mistakes are different - not applying a lot of complex logic to come to an incorrect deduction/guess (= LLM hallucination), but rather just shallow recall/guess. e.g. An LLM would guess/hallucinate a capital city by using rules it had learnt about other capital cities - must be famous, large, perhaps have an airport, etc, etc; a human might just use "famous" to guess, or maybe just throw out the name of the only city they can associate to some country/state.
The human would often be aware that they are just guessing, maybe based on not remembering where/how they had learnt this "fact", but to the LLM it's all just statistics and it has no episodic memory (or even coherent training data - it's all sliced and diced into shortish context-length samples) to ground what it knows or does not know.