I feel like more and more LLM use is inevitable, and at least some of it is no different from the age-old (as far as “old” applies to LessWrong and online forums) problem of filtering new users who are more enthusiastic than organized, and generate a lot of spew before really slowing down and writing FOR THIS AUDIENCE (or getting mad and going away, in the case of bad fit). LLMs make it easy to increase that volume of low-value stuff.
I really want to enable the high-value uses of LLM, because I want more from many posts and I think LLMs CAN BE a good writing partner. My mental model is that binary approaches (“identify LLM-generated content”) are going to fail, because the incentives are wrong, but also because it discourages the good uses.
The problem of voluminous bad posts has two main tactics for us to use against it. 1) identification and filtering (downvoting and admin intervention). This works today (though it’s time-consuming and uncomfortable), and is likely to continue to work for quite some time. I haven’t really played with LLM as evaluation/summary for things I read and comment on, but I think I’m going to try it. I wonder if I can get GPT to estimate how much effort went into a post...
2) assistance and encouragement of posters to think and write for this audience, and to engage with comments (which are sometimes very direct). This COULD include recommendations for LLM critiques or assistance with organization or presentation, along with warnings that LLMs can’t actually predict your ideas or reason for posting—you have to prompt it well and concretely.
We need both of these, and I’m not sure the balance changes all that much in an LLM world.
I feel like more and more LLM use is inevitable, and at least some of it is no different from the age-old (as far as “old” applies to LessWrong and online forums) problem of filtering new users who are more enthusiastic than organized, and generate a lot of spew before really slowing down and writing FOR THIS AUDIENCE (or getting mad and going away, in the case of bad fit). LLMs make it easy to increase that volume of low-value stuff.
I really want to enable the high-value uses of LLM, because I want more from many posts and I think LLMs CAN BE a good writing partner. My mental model is that binary approaches (“identify LLM-generated content”) are going to fail, because the incentives are wrong, but also because it discourages the good uses.
The problem of voluminous bad posts has two main tactics for us to use against it.
1) identification and filtering (downvoting and admin intervention). This works today (though it’s time-consuming and uncomfortable), and is likely to continue to work for quite some time. I haven’t really played with LLM as evaluation/summary for things I read and comment on, but I think I’m going to try it. I wonder if I can get GPT to estimate how much effort went into a post...
2) assistance and encouragement of posters to think and write for this audience, and to engage with comments (which are sometimes very direct). This COULD include recommendations for LLM critiques or assistance with organization or presentation, along with warnings that LLMs can’t actually predict your ideas or reason for posting—you have to prompt it well and concretely.
We need both of these, and I’m not sure the balance changes all that much in an LLM world.