> It seems unfair to call out LLMs for "spam, scams, propaganda, and astroturfing." These problems are largely the result of platform optimization for engagement and SEO competition for attention.
They didn't create those markets, but they're the markets for which LLMs enhance productivity and capability the best right now, because they're the ones that need the least supervision of input to and output from the LLMs, and they happen to be otherwise well-suited to the kind of work it is, besides.
> This isn't unique to models; even we, humans, when operating without feedback, generate mostly slop.
I don't understand the relevance of this.
> Curation is performed by the environment and the passage of time, which reveals consequences.
It'd say it's revealed by human judgement and eroded by chance, but either way, I still don't get the relevance.
> LLMs taken in isolation from their environment are just as sloppy as brains in a similar situation.
Sure? And clouds are often fluffy. Water is often wet. Relevance?
The rest of this is a description of how we can make LLMs work better, which amounts to more work than required to make LLMs pay off enormously for the purposes I called out, so... are we even in disagreement? I don't disagree that perhaps this will change, and explicitly bound my original claim ("so far") for that reason.
... are you actually demonstrating my point, on purpose, by responding with LLM slop?
LLMs can generate slop if used without good feedback or trying to minimize human contribution. But the same LLMs can filter out the dark patterns. They can use search and compare against dozens or hundreds of web pages, which is like the deep research mode outputs. These reports can still contain mistakes, but we can iterate - generate multiple deep reports from different models with different web search tools, and then do comparative analysis once more. There is no reason we should consume raw web full of "spam, scams, propaganda, and astroturfing" today.
They didn't create those markets, but they're the markets for which LLMs enhance productivity and capability the best right now, because they're the ones that need the least supervision of input to and output from the LLMs, and they happen to be otherwise well-suited to the kind of work it is, besides.
> This isn't unique to models; even we, humans, when operating without feedback, generate mostly slop.
I don't understand the relevance of this.
> Curation is performed by the environment and the passage of time, which reveals consequences.
It'd say it's revealed by human judgement and eroded by chance, but either way, I still don't get the relevance.
> LLMs taken in isolation from their environment are just as sloppy as brains in a similar situation.
Sure? And clouds are often fluffy. Water is often wet. Relevance?
The rest of this is a description of how we can make LLMs work better, which amounts to more work than required to make LLMs pay off enormously for the purposes I called out, so... are we even in disagreement? I don't disagree that perhaps this will change, and explicitly bound my original claim ("so far") for that reason.
... are you actually demonstrating my point, on purpose, by responding with LLM slop?