Opening up ChatGPT: Tracking openness, transparency, and accountability
  in instruction-tuned text generators
Andreas Liesenfeld Alianda Lopez Mark Dingemanse
Large language models that exhibit instruction-following behaviour represent
one of the biggest recent upheavals in conversational interfaces, a trend in
large part fuelled by the release of OpenAI's ChatGPT, a proprietary large
language model for text generation fine-tuned through reinforcement learning
from human feedback (LLM+RLHF). We review the risks of relying on proprietary
software and survey the first crop of open-source projects of comparable
architecture and functionality. The main contribution of this paper is to show
that openness is differentiated, and to offer scientific documentation of
degrees of openness in this fast-moving field. We evaluate projects in terms of
openness of code, training data, model weights, RLHF data, licensing,
scientific documentation, and access methods. We find that while there is a
fast-growing list of projects billing themselves as 'open source', many inherit
undocumented data of dubious legality, few share the all-important
instruction-tuning (a key site where human annotation labour is involved), and
careful scientific documentation is exceedingly rare. Degrees of openness are
relevant to fairness and accountability at all points, from data collection and
curation to model architecture, and from training and fine-tuning to release
and deployment.