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Kwpolskayesterday at 8:15 PM8 repliesview on HN

Fable might be using those phrases less, but its writing is still terrible and exhausting to read.


Replies

szniotoday at 5:16 AM

If it's "read as an article" or something then yeah it's crap and Fable's current style isn't actually better than older models. For flowery speech old models are perfectly fine.

For quickly parsing the agent output, it's formulaism isn't a bad thing.

Fable's writing does have a property of going over my head, which didn't happen with earlier agents. Asking for clarification doesn't really give good results.

We've gone full circle where I once again use classical search just to look up what the fuck it's yapping about. It's much quicker and more accurate to take a glance at Wikipedia, than to ask the agent.

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preg_matchtoday at 3:37 PM

Yes, these models are very good at writing code but they absolutely suck ass at prose. The prose is annoying and repetitive. At least we only have to deal with it in prompting if you're writing code.

Oh, and comments. You have to do a good amount of prompting to not get shitty 10-line-long comments everywhere.

jnwatsontoday at 4:51 AM

I recently wrote a short paper with Fable, and, with some prodding, I was able to get some non-painful prose out of it.

I just found my prompt:

The writing style could really use some work. Avoid Claude-isms like "stated fairly", em dashes, "load-bearing", overly punchy phrasing like "keep the signal, govern the response". This is a technical document, not a marketing campaign.

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arizenyesterday at 8:48 PM

Could these complex/hard to read Fable outputs be sign of some kind of industrial level of intelligence, which us humans may have a hard to comprehend, while it may be also hard for machine to use simpler texts to properly outline all nuances and complexities of concepts it output?

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wfmeyesterday at 9:59 PM

Agreed. I’ve interestingly found 5.6 sol to produce much better writing, and it can generally cut to the point much more effectively.

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merlindruyesterday at 11:04 PM

models are hungry for a more information-dense language

for now all they've got is english, so they'll just bend that into shape. it'll do.

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kingkawntoday at 12:46 PM

The exhausting worthlessness of all LLM writing is so palpable that we need a new theory of the value of culture that has no relationship to the content. Back to the old Aura of the Artist arguments from the Industrial Revolution

markab21yesterday at 10:38 PM

Yes - THIS! I can't even believe how exhausting it is to read. I'm not sure why or what changed in Fable. Did they do this writing-style output to give it more token compression during/for training or to prefer output for less money?

I love it for a few things, but it's gotten really hard to spend any extended amount of time with it because of the lack of mental model I seem to be able to hold while working with complicated problems.

I'm guessing it's just not enough time doing RL on human feedback.

Check out the anouncement of Inkling (https://thinkingmachines.ai/news/introducing-inkling/)... the section in the middle

"Early in RL verbose, grammatical" (if you search) :

We need to understand the operator. The 5D line element is ds² = e^{2A(x)} (ds²_4d + dx²), where A(x) = sin(x) + 4 cos(x), x in [0, 2π]. The internal coordinate is periodic. The background is a warped product: metric g_{MN} where M,N = 0..4. The internal direction has metric e^{2A(x)} dx²? Wait, the ds² is e^{2A} (ds²_4d + dx²). So the internal metric is e^{2A(x)} dx². Actually if the total metric is ds² = e^{2A(x)} (ds²_4d + dx²), then yes, internal metric is e^{2A} dx².

vs. Post RL

We need determine eigenvalue problem for spin-2 fluctuations h_{μν}(x,y) with TT in 4d and depend on x. For metric of form ds² = e^{2A(x)} (g_{μν}(y) + h_{μν}(y,x)) dy^μ dy^ν + e^{2A(x)}? Wait internal metric is e^{2A} dx²? Actually ds² = e^{2A} [ds_4² + dx²]. So internal metric is e^{2A} dx²; warp factor same for 4d and internal? Yes. We need equation for h_{μν}(y,x) = h_{μν}(y) ψ(x) maybe with normalization. …

I can understand it with less cognitive load in the post-RL version versus early in RL. This resonated with my experience using Fable, especially digging hard problems; it feels like I'm reading the "early in RL" version of that model explanation.