Another concern is that training such models on
huge datasets will almost inevitably require them to
have undesirable content in their training sets, such
as that promoting hateful stereotypes (Christian,
2020). Having models output undesirable content
is, by deﬁnition, undesirable, but we believe that
attacking this problem from the training set side
is unproductive and ultimately leads us away from
optimal solutions. If a person reads a racist piece
of content, they do not then immediately adopt its
racist views—they may be capable of doing so, but
can decide not to. This capacity to understand un-
desirable content and then decide to ignore it is an
essential future research direction. Not only would
this allow models to use “dirtier" data with less
concern, but also to use their gained knowledge
to better understand what not to do. We recognize
that, despite recent progress in human-guided learn-
ing (Stiennon et al., 2020), the technology is not
The Pile- An 800GB Dataset of Diverse Text for Language Modeling