98 citations · 108 across the 5 of their papers we have counts for
5 papers
Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts
Mounica Maddela, Megan Ung, Jing Xu +3
Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack tr…
Improving Open Language Models by Learning from Organic Interactions
Jing Xu, Da Ju, Joshua Lane +10
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 syst…
Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback
Jing Xu, Megan Ung, Mojtaba Komeili +3
Frozen models trained to mimic static datasets can never improve their performance. Models that can employ internet-retrieval for up-to-date information and obtain feedback from hu…
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
Kurt Shuster, Jing Xu, Mojtaba Komeili +15
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large…
SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures
Megan Ung, Jing Xu, Y-Lan Boureau
Current open-domain conversational models can easily be made to talk in inadequate ways. Online learning from conversational feedback given by the conversation partner is a promisi…