5.2k citations · 10k across the 79 of their papers we have counts for
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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…
DIRECTOR: Generator-Classifiers For Supervised Language Modeling
Kushal Arora, Kurt Shuster, Sainbayar Sukhbaatar +1
Current language models achieve low perplexity but their resulting generations still suffer from toxic responses, repetitiveness and contradictions. The standard language modeling…