I don't think LLMs can self-correct without remembering their own training in some way.
(If someone tries this and it works, I’m quitting my phd and going back to camp counseling)
So if one were to improve an LLM along those lines, I believe it would be something like: 1) LLM is asked a question. 2) LLM comes up with an initial response. 3) LLM retrieves the related "learning" history behind that answer and related portions of the corpus. 4) LLM compares the initial answer with the richer set of information, looking for conflicts between the initial answer and the broader set, or "learning" choices that may be false. 6) LLM generates a better answer and gives it. 7) LLM incorporates this new "learning".
And that strikes me as a pretty reasonable long-term approach, if not one that fits within the constraints of the current gold rush.
From the abstract:
> ... To give LLMs such ability, we explore source-aware training -- a recipe that involves (i) training the LLM to associate unique source document identifiers with the knowledge in each document, followed by (ii) an instruction-tuning stage to teach the LLM to cite a supporting pretraining source when prompted.
See also: https://www.sciencedirect.com/science/article/pii/S157106452... o1's training regime is described by the "strange particle" model in this formulation
But even if it's embedded in a framework, say CS, the qualia fade in the background as time passes. E.g. like everybody in CS, I'm pretty much able to quote O() performance characteristics of a sizeable number of algorithms off the bat. If you ask me where I learned it, for that specific algorithm - that's long receded into the past.
When humans self-correct, the normal process isn't "gauging whether you know the thing" or the even more impressive feat of calling up if you heard it from a "less than reliable source". There's a fuzzy sense of "I don't fully understand it", and self-correction means re-verifying the info from a trusted source.
So, no, I don't think the qualia matter for recall as much as you think.
About the terms themselves, "confabulate" means "exchanging stories", while "hallucinate" is less clear but probably means "to err". In psychiatry, "hallucinate" was apparently introduced by Esquirol and "confabulate" by Wernicke and Bonhoeffer; neither concept seems to be akin to the substance of the phenomenon of "stochastic parrots bullshitting an unchecked narrative through formal plausibility".
See: "Hallucinations and related concepts - their conceptual background" - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4515540/
and: "The Confabulating Mind: How the Brain Creates Reality" - https://psychiatryonline.org/doi/full/10.1176/appi.ajp.2008....
I propose:
- digimagination
- stochafubalation
- statillucination
- matmulshitting
Let's ask chatgpt free (coz I'm cheap) what it not-thinks about these, and ask for more statistically generated bullshit:
-- 8< --
These are some creative and fun suggestions! Here's my take:
Digimagination: This one has a sleek, almost sci-fi ring to it. It captures the digital, machine-driven nature of language model "hallucination," but presents it in a positive, imaginative light. This could highlight the creative aspect of AI responses, even when they aren't grounded in fact.
Stochafubalation: Combining "stochastic" (random probability) with "confabulation" is clever. It emphasizes the statistical nature of language models making up facts, though it could be a bit of a mouthful.
Statillucination: I like how this merges "statistical" and "hallucination." It feels technical and would fit right into the AI research community's vocabulary.
Matmulshitting: This one is tongue-in-cheek and hilarious! It references matrix multiplication (which drives many neural network operations) with a humorous, blunt twist. Probably not for formal use, but great for informal contexts!
Probafabrication: A combination of "probability" and "fabrication," highlighting the statistical underpinnings of how LLMs generate falsehoods.
Algoillusion: A fusion of "algorithm" and "illusion," suggesting the misleading or illusionary nature of generated information.
Synthifalse: A contraction of "synthetic" and "false," pointing to AI-generated responses that are not grounded in reality.
Pseudoformulation: Emphasizing that the response is a pseudo (false) formulation rather than a real answer.
Modelucination: A blend of "model" and "hallucination," capturing the fact that the machine learning model is generating false or misleading data.My judgement of yours: I love 'stochfabulating' and 'matmulshitting', but I condemn 'digimagining'. Digimagining is doable in proper terms (no pun intended).