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20212024
most citedBlenderBot 3: a deployed conversational agent that continually learns to responsibly engage

98 citations · 112 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024

Deliberation in Latent Space via Differentiable Cache Augmentation

Luyang Liu, Jonas Pfeiffer, Jiaxing Wu +2

Techniques enabling large language models (LLMs) to "think more" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However…

cs.CL20239 cited

Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models

Jimmy Wei, Kurt Shuster, Arthur Szlam +3

Current dialogue research primarily studies pairwise (two-party) conversations, and does not address the everyday setting where more than two speakers converse together. In this wo…

cs.CL2023

Infusing Commonsense World Models with Graph Knowledge

Alexander Gurung, Mojtaba Komeili, Arthur Szlam +2

While language models have become more capable of producing compelling language, we find there are still gaps in maintaining consistency, especially when describing events in a dyn…

cs.CL202298 cited

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…

cs.CL20213 cited

Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity

Kurt Shuster, Jack Urbanek, Arthur Szlam +1

State-of-the-art dialogue models still often stumble with regards to factual accuracy and self-contradiction. Anecdotally, they have been observed to fail to maintain character ide…