98 citations · 259 across the 11 of their papers we have counts for
12 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…
Continual Dialogue State Tracking via Example-Guided Question Answering
Hyundong Cho, Andrea Madotto, Zhaojiang Lin +5
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on prev…
The CRINGE Loss: Learning what language not to model
Leonard Adolphs, Tianyu Gao, Jing Xu +3
Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples. Growing evidence shows that even w…
When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels
Weiyan Shi, Emily Dinan, Kurt Shuster +2
Deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves. However, humans may not always provide explicit signals when the chatbot…
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…
Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls
Da Ju, Jing Xu, Y-Lan Boureau +1
The promise of interaction between intelligent conversational agents and humans is that models can learn from such feedback in order to improve. Unfortunately, such exchanges in th…
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…
Beyond Goldfish Memory: Long-Term Open-Domain Conversation
Jing Xu, Arthur Szlam, Jason Weston
Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. In contrast, the long-term…
Recipes for Safety in Open-domain Chatbots
Jing Xu, Da Ju, Margaret Li +3
Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted bi…
Recipes for building an open-domain chatbot
Stephen Roller, Emily Dinan, Naman Goyal +9
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of…