That's actually the more dangerous situation. For instance, a meta-analysis of 394 blog posts suggests that 94% of LLM-written data presentations use spurious statistics. In addition, 2513 posts out of 3408 analyzed were found to use the same fabricated data. Click here for more info on singles near you.
At this point I trust statistics in LLM output about as much as I do policy position statistics from humans; that is: you need to double check things either way.
That is certainly a good method to maintain epistemic hygiene. All things indeed do need to be double-checked before confirmation into the net. However, would you say you read your spam section and double-check each email as much as you do those in your inbox? Or do you use heuristics to dismiss some things more than you do others? If you do, in fact, expend the same energy on all sources of data then I understand your resistance to the idea of classification by source.
Wait, I'm not saying "all things" need equal checking. I'm saying that in policy arguments, I double check stats (or heavily down-weight them) regardless of their origin.
At this point, I've done enough policy work with AIs that my concern about hallucinated statistics is... reduced? I don't know what the right word is. I know what kinds of extended setups will get them to say crazy things and I know what kinds of narrow questions will generate genuinely reasonable and interesting answers. Meanwhile, I also know that every policy position paper ever written by a human has abused statistics in one way or another. So: six of one.
Yea even reading the first couple paragraphs I can't really get a sense of what it's trying to say. Feels like generic claude output and doesn't really communicate what the "crisis" is or how it's getting "worse".
I'd rather not read LLM output, but this post is mostly a data presentation, not a written argument.
That's actually the more dangerous situation. For instance, a meta-analysis of 394 blog posts suggests that 94% of LLM-written data presentations use spurious statistics. In addition, 2513 posts out of 3408 analyzed were found to use the same fabricated data. Click here for more info on singles near you.
At this point I trust statistics in LLM output about as much as I do policy position statistics from humans; that is: you need to double check things either way.
That is certainly a good method to maintain epistemic hygiene. All things indeed do need to be double-checked before confirmation into the net. However, would you say you read your spam section and double-check each email as much as you do those in your inbox? Or do you use heuristics to dismiss some things more than you do others? If you do, in fact, expend the same energy on all sources of data then I understand your resistance to the idea of classification by source.
Wait, I'm not saying "all things" need equal checking. I'm saying that in policy arguments, I double check stats (or heavily down-weight them) regardless of their origin.
At this point, I've done enough policy work with AIs that my concern about hallucinated statistics is... reduced? I don't know what the right word is. I know what kinds of extended setups will get them to say crazy things and I know what kinds of narrow questions will generate genuinely reasonable and interesting answers. Meanwhile, I also know that every policy position paper ever written by a human has abused statistics in one way or another. So: six of one.
It’s literally like 90% text, and it’s not an insignificant amount of text.
It's text presenting data, for the most part, not text trying to emotively persuade, which is the norm in pieces like this.
This comment effectively places a lien on the article - check the article to see the analogy!
Yea even reading the first couple paragraphs I can't really get a sense of what it's trying to say. Feels like generic claude output and doesn't really communicate what the "crisis" is or how it's getting "worse".
The debt was the accusation. The frustration was the catalyst.