Improving Open Language Models by Learning from Organic Interactions
Authors: name: Jing Xu, name: Da Ju, name: Joshua Lane, name: Mojtaba Komeili, name: Eric Michael Smith, name: Megan Ung, name: Morteza Behrooz, name: William Ngan, name: Rashel Moritz, name: Sainbayar Sukhbaatar, name: Y-Lan Boureau, name: Jason Weston, name: Kurt Shuster

We present BlenderBot 3x, an update on the conversational model BlenderBot 3,
which is now trained using organic conversation and feedback data from
participating users of the system in order to improve both its skills and
safety. We are publicly releasing the participating de-identified interaction
data for use by the research community, in order to spur further progress.
Training models with organic data is challenging because interactions with
people "in the wild" include both high quality conversations and feedback, as
well as adversarial and toxic behavior. We study techniques that enable
learning from helpful teachers while avoiding learning from people who are
trying to trick the model into unhelpful or toxic responses. BlenderBot 3x is
both preferred in conversation to BlenderBot 3, and is shown to produce safer
responses in challenging situations. While our current models are still far
from perfect, we believe further improvement can be achieved by continued use
of the techniques explored in this work.