alt Hacker NewsNewest LLM writing tell: Concepts are described in terms normally more appropriate for physical object.
> A lab that suppresses it in a frontier model just moves the advantage to open models that still _carry_ it
> they carry no signal about which is better
> where your workload _sits_ on that frontier should pick the point
> and no model _sits_ in the judge’s seat
> every ratio _sits_ at 0.99–1.10
Many many more examples of "sit"
> Every comparison in this post "holds" the questions
I have been seeing this a lot in my recent work with LLMs and it is quite frustrating. Even more frustrating is how frequently it uses low-signal terms for things unnecessarily. These 'physical object' terms are one example but at times it really seems that they 'preserve effort' by choosing a less descriptive term because it 'fits'
I have also caught it replacing descriptive terms with more vague ones for no discernible reason other than laziness.
"Minimize ambiguity" has been my go-to instruction as of late when the agent drifts back towards vague terms and lack of specificity.
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By now I am allergic to the word "carry", I just cannot continue reading any more.
Oh crap, if these are the new LLM tells then a lot of people are going to start accusing me of AI writing...
I have a strong tendency of talking about concepts like they're physical objects. A lot of the people I know IRL do too, so it might be a regional thing idk.
>"Minimize ambiguity" has been my go-to instruction as of late when the agent drifts back towards vague terms and lack of specificity.
Anthropic has called the greater category containing this type of writing "mannered prose" https://platform.claude.com/docs/en/build-with-claude/prompt...
If you ask the models to avoid mannered prose (or use their extended prompt), it basically eliminates all of this type of slop writing.
Here's a de-slopped example.
> Write Like It's 1866: LLMs Relearn Telegraphese
> Adding one sentence to a prompt, telling the model to write like a telegram, cut its output tokens by 40–49%. The sentence asks it to drop articles and filler but keep every fact. Models from four different labs then answered questions from that compressed text as accurately as from normal English. So when one model writes something for another model to read, you pay about half as much for the output. This post introduces the Telegraph Test, a benchmark that measures how well a given model does this.
Cool story bro. Maybe you could engage with the content? I'm an actual person.
For me it's the obsession with the universal quantifier. Even in these examples: "no model", "every ratio", "every comparison". They love emphasizing that everything in a set meets some condition. I assume it's an effect of being trained on coding tasks where they need to make sure that all cases are handled.