I agree.

I've come to believe this is also a side effect of the desire for less (/goal: no) human in the loop on the part of the people driving all this capex spend. I think if you actually want to manually review output there will be a moment where you will actually want a separate interface to a stupider or "simpler" model. I suspect sometimes dealing with Fable 5 that this threshold has already been crossed. It's not that the raw code output is so good, it's that it just doesn't speak to me in a way I would like. Perhaps the verbosity is worthwhile when generating code as a sort of first pass some other model can auto or adversarially chop down. The best place for a human is probably outside of this part of the loop all together.

So I might as well just let it auto /goal it's own thing with sufficient constraints while myself and a model that can converse in parallel with less "deictic" (thanks for this word btw) volume as you put it for the areas of the code where I want to "frame" the vocabulary or where my personal understanding is of high value. I know people already do this in many ways, like use one company's model for planning and another for coding. It just feels inevitable at a certain point that the "natural language" output of LLMs writing the bulk of the code is not targeted towards humans. And really, why should it be?

It's usually a pretty clear case of reaching for statements that both sound impressive while also being broad and vague enough they are less likely to be factually wrong. In this regime, being difficult to parse is actually part of the performance, because it prevents the user from being able to spot a clear error.

By "decompress," "eliminate deictic language," etc. are you trying to say "speak simply and clearly?"

Eschew obfuscation...

The effect you describe reminds me of reading Edward W. Said's "Orientalism" when I was younger. Fable suddenly started with this kind of lingo, iirc, and Opus 5 sounds exactly the same. Tin foil: it's ultimately a vendor lock-in strategy, you'll get the best results with agents from the same tribe, others will trip over the mountain of idiosyncratic metaphors.

> You ever read a work of literature with such flowery language that right after you've read a paragraph, you pause and realize you have no clue what you actually read, only to read the paragraph maybe a second or third time and have your mind space out again and again on each successive attempt?

I've probably read Jack Vance's Dying Earth Series 3 times; Even though I've only sat and read it once in reality. This also points out the problem of important details along side the fluff.

If only I enjoyed LLMs prose as much as I do Vance's.

Absolutely! And at some point seems as if one is looking at this weird line noise, which seems like text, but is random at best and then makes zero sense. I typically wonder if it is a sign of a burnout on my side or all of it is like it.

There are days where this impression/feeling of meaningless in the text seems particularly strong.

I am constantly asking Claude to be more terse/brief/ELI5. Improve using the tool. But if I have to scroll to read the output I just can’t follow it.

If I see a long paragraph and I know the author is Neal Stephenson I think “this is going to be dense but good.” LLM long outputs on a code base I know well just make me glassy eyed.

> You ever read a work of literature with such flowery language that right after you've read a paragraph, you pause and realize you have no clue what you actually read, only to read the paragraph maybe a second or third time and have your mind space out again and again on each successive attempt?

Any recent work by William Gibson matches the description.

The way I managed to get claude to stop doing this is telling it "This document is for you for later use, no need to over explain things or extra verbosity"

You have to tell it to not use LLMisms and stupid metaphors. The serious-but-empathetic-sounding fluffy metaphors get on my nerves, and sometimes can overlap with something technical you are learning such that you can't tell if it's a new term or not.

Yeah yeah. Except for me its this feeling most of the time. Everything it generates is grammatically correct, phrased tight, emdashed to death and nothing is wrong as such, and yet what i just read contributes absolute 0 to why i started the chat in the first place. Its mind bendingly similar to a work equivalent of doom scrolling is what it is.

I'm coping.

The output from newer frontier models of Anthropic and Openai are so easily detectable as AI it's getting laughable. They constantly produce a huge wall of text no human expert on a specific topic would ever write. Extreme overuse of jargon and invented terms / metaphors makes me believe the people hired for RLHF aren't actually experts on their subject matter which seems plausible to me as real experts wouldn't do such a job for regular pay

agree and i wonder why don't they (the leading AI labs that are putting out these models) fix this? I felt it shouldn't be too hard to introduce some bias towards more intelligible text during the post training/fine-tuning stages?

I noticed this with Claude a lot. Fable sounds often like a philosopher. Some conversations reminded me of books by Kant.

All that flowery, descriptive, metaphor laden language has a point.

It is running up your bill.

I will say this again: LLM's tokens are just B2B Gacha.

opus 5 are so bad on this. It often explain it too verbose, and include other things that isn't in the focus but related. ADHD mode helps me greatly on this, though there are some information loss in it.

This has recently become a pretty pressing issue for me, as it's starting to severely hinder my ability to be productive with the models. It's hard to tell if its getting worse with every model release, specific to Anthropic's models, a reflection of my ADHD, all/none of the above, but holy shit do I get aggravated when I'm forced to parse the most unintelligible, jargon-dense bullshit explanations in whatever the model output is. And then I feel silly getting genuinely tilted by the model's inability to just... explain something semi-normally, without it requiring me to berate it into simplicity.

For some discrete skills I use, I include a final step on the the output that runs through 1+ subagents to de-slop the text and to actually simplify it, but so far nothing has worked as well I've hoped. Considering hopping off Anthropic's models to try out others to see if they're less egregious.

I feel we can get around this. Either in the system prompt telling claude to dumb down the language or training the model itself to talk in more layman terms to bring us to understanding rather then assuming we understand much of it already. Also maybe training the LLM to get to know how much we know before responding.

Well, now I had to ask an LLM to give me examples of what "deictic language" means...