An LLM is a lossy encyclopedia

When you have a lossy piece of media, such as a compressed sound or image file, you can always see the resemblance to the original and note the degradation as it happens. You never have a clear JPEG of a lamp, compress it, and get a clear image of the Milky Way, then reopen the image and get a clear image of a pile of dirt.

Furthermore, an encyclopaedia is something you can reference and learn from without a goal, it allows you to peruse information you have no concept of. Not so with LLMs, which you have to query to get an answer.

In compressed audio these can be things like clicks and boings and echoes and pre-echoes. In compressed images they can be ripply effects near edges, banding in smoothly varying regions, but there are also things like https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres... where one digit is replaced with a nice clean version of a different digit, which is pretty on-the-nose for the LLM failure mode you're talking about.

Compression artefacts generally affect small parts of the image or audio or video rather than replacing the whole thing -- but in the analogy, "the whole thing" is an encyclopaedia and the artefacts are affecting little bits of that.

Of course the analogy isn't exact. That would be why S.W. opens his post by saying "Since I love collecting questionable analogies for LLMs,".

It was quickly discovered that LLMs are capable of re-checking their own solutions if prompted - and, with the right prompts, are capable of spotting and correcting their own errors at a significantly-greater-than-chance rate. They just don't do it unprompted.

Eventually, it was found that reasoning RLVR consistently gets LLMs to check themselves and backtrack. It was also confirmed that this latent "error detection and correction" capability is present even at base model level, but is almost never exposed - not in base models and not in non-reasoning instruct-tuned LLMs.

The hypothesis I subscribe to is that any LLM has a strong "character self-consistency drive". This makes it reluctant to say "wait, no, maybe I was wrong just now", even if latent awareness of "past reasoning look sketchy as fuck" is already present within the LLM. Reasoning RLVR encourages going against that drive and utilizing those latent error-correction capabilities.

"Language, Halliday argues, "cannot be equated with 'the set of all grammatical sentences', whether that set is conceived of as finite or infinite". He rejects the use of formal logic in linguistic theories as "irrelevant to the understanding of language" and the use of such approaches as "disastrous for linguistics"."

CS never solved the incoherence of language, conduit metaphor paradox. It's stuck behind language's bottleneck, and it do so willingly blind-eyed.

You weren't talking to GPT-4o about philosophy recently, were you?

Beyond this point engineers actually have to know what signaling is, rather than 'information.'

https://www.sciencedirect.com/science/article/abs/pii/S00033...

Ultimately, engineering chose the wrong approach to automating language, and it sinks the field. It's irreversible.

If language doesn't really mean anything, then automating it in geometry is worse than problematic.

The solution is starting over at 1947: measurement not counting.