TL;DR: For the record, EleutherAI never actually had a policy of always releasing everything to begin with and has always tried to consider each publication’s pros vs cons. But this is still a bit of change from EleutherAI, mostly because we think it’s good to be more intentional about what should or should not be published, even if one does end up publishing many things. EleutherAI is unaffected and will continue working open source. Conjecture will not be publishing ML models by default, but may do so on a case by case basis.
Our decision to open-source and release the weights of large language models was not a haphazard one, but was something we thought very carefully about. You can read my short post here on our reasoning behind releasing some of our models. The short version is that we think that the danger of large language models comes from the knowledge that they’re possible, and that scaling laws are true. We think that by giving researchers access to the weights of LLMs, we will aid interpretability and alignment research more than we will negatively impact timelines. At Conjecture, we aren’t against publishing, but by making non-disclosure the default, we force ourselves to consider the long-term impact of each piece of research and have a better ability to decide not to publicize something rather than having to do retroactive damage control.
What is the reasoning behind non-disclosure by default? It seems opposite to what EleutherAI does.
See a longer answer here.
TL;DR: For the record, EleutherAI never actually had a policy of always releasing everything to begin with and has always tried to consider each publication’s pros vs cons. But this is still a bit of change from EleutherAI, mostly because we think it’s good to be more intentional about what should or should not be published, even if one does end up publishing many things. EleutherAI is unaffected and will continue working open source. Conjecture will not be publishing ML models by default, but may do so on a case by case basis.
Our decision to open-source and release the weights of large language models was not a haphazard one, but was something we thought very carefully about. You can read my short post here on our reasoning behind releasing some of our models. The short version is that we think that the danger of large language models comes from the knowledge that they’re possible, and that scaling laws are true. We think that by giving researchers access to the weights of LLMs, we will aid interpretability and alignment research more than we will negatively impact timelines. At Conjecture, we aren’t against publishing, but by making non-disclosure the default, we force ourselves to consider the long-term impact of each piece of research and have a better ability to decide not to publicize something rather than having to do retroactive damage control.