3.7k citations · 11.8k across the 85 of their papers we have counts for
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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…
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue
Leonard Adolphs, Kurt Shuster, Jack Urbanek +2
Large language models can produce fluent dialogue but often hallucinate factual inaccuracies. While retrieval-augmented models help alleviate this issue, they still face a difficul…
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Julia Kiseleva, Ziming Li, Mohammad Aliannejadi +12
Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…
Hash Layers For Large Sparse Models
Stephen Roller, Sainbayar Sukhbaatar, Arthur Szlam +1
We investigate the training of sparse layers that use different parameters for different inputs based on hashing in large Transformer models. Specifically, we modify the feedforwar…
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…
Not All Memories are Created Equal: Learning to Forget by Expiring
Sainbayar Sukhbaatar, Da Ju, Spencer Poff +4
Attention mechanisms have shown promising results in sequence modeling tasks that require long-term memory. Recent work investigated mechanisms to reduce the computational cost of…
droidlet: modular, heterogenous, multi-modal agents
Anurag Pratik, Soumith Chintala, Kavya Srinet +10
In recent years, there have been significant advances in building end-to-end Machine Learning (ML) systems that learn at scale. But most of these systems are: (a) isolated (percept…