We are actually currently in a sprint where we are experimenting with integrating LLM systems directly into LW in various ways.
A thing I’ve been thinking about is to build tools on LW so that it’s easy to embed LLM generated content, but in a way where any reader can see the history of how that content was generated (i.e. seeing whatever prompt or conversational history lead to that output). My hope would be that instead of people introducing lots of LLM-slop and LLM-style-errors into their reasoning, the experience of the threads where people use LLMs becomes more one of “collectively looking at the output of the LLM and being curious about why it gave that answer”, which I feel like has better epistemic and discourse effects.
We’ve also been working with base models and completion models where the aim is to get LLM output that really sound like you and picks up on your reasoning patterns, and also more broadly understands what kind of writing we want on LW.
This is all in relatively early stages but we are thinking pretty actively about it.
That’s awesome. One of my worries about this (which applies to most harm-reduction programs) is that I’d rather have less current-quality-LLM-generated stuff on LW overall, and making it a first-class feature makes it seem like I want more of it.
Having a very transparent not-the-same-as-a-post mechanism solves this worry very well.
We are actually currently in a sprint where we are experimenting with integrating LLM systems directly into LW in various ways.
A thing I’ve been thinking about is to build tools on LW so that it’s easy to embed LLM generated content, but in a way where any reader can see the history of how that content was generated (i.e. seeing whatever prompt or conversational history lead to that output). My hope would be that instead of people introducing lots of LLM-slop and LLM-style-errors into their reasoning, the experience of the threads where people use LLMs becomes more one of “collectively looking at the output of the LLM and being curious about why it gave that answer”, which I feel like has better epistemic and discourse effects.
We’ve also been working with base models and completion models where the aim is to get LLM output that really sound like you and picks up on your reasoning patterns, and also more broadly understands what kind of writing we want on LW.
This is all in relatively early stages but we are thinking pretty actively about it.
That’s awesome. One of my worries about this (which applies to most harm-reduction programs) is that I’d rather have less current-quality-LLM-generated stuff on LW overall, and making it a first-class feature makes it seem like I want more of it.
Having a very transparent not-the-same-as-a-post mechanism solves this worry very well.