5.2k citations · 10k across the 79 of their papers we have counts for
5 papers · 2 filters
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…
NormFormer: Improved Transformer Pretraining with Extra Normalization
Sam Shleifer, Jason Weston, Myle Ott
During pretraining, the Pre-LayerNorm transformer suffers from a gradient magnitude mismatch: gradients at early layers are much larger than at later layers. These issues can be al…
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…
Retrieval Augmentation Reduces Hallucination in Conversation
Kurt Shuster, Spencer Poff, Moya Chen +2
Despite showing increasingly human-like conversational abilities, state-of-the-art dialogue models often suffer from factual incorrectness and hallucination of knowledge (Roller et…